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"content": "{\"data\":\"Skip to content\\nNo results\\nAI Advanced Articles\\nAI Tools Articles\\nTop YouTube Channels to Master AI\\nTriple C Engineering\\nLearn AI Articles\\nAI News\\nPhysical Address\\n304 North Cardinal St.\\nDorchester Center, MA 02124\\nTriple C Engineering\\nLearn AI Articles\\nAI Tools Articles\\nAI Advanced Articles\\nAI News\\nTop YouTube Channels to Master AI\\nSearch\\nMenu\\nUnderstanding Machine Learning and Its Cloud Applications\\nwpai\\nJune 12, 2025\\nLearn AI Articles\\nMachine Learning in Cloud Computing: An In-Depth Exploration\\nMeta Summary: Discover the synergy between machine learning and cloud computing, a powerful combination reshaping industries through scalable insights, automation, and predictive analytics.\\nIntroduction to Machine Learning\\nMachine learning, a transformative arm of artificial intelligence, forms the backbone of numerous technological breakthroughs. Its fusion with cloud computing has unlocked scalable resources, revolutionizing data analysis and processing. This in-depth article targets technical professionals, sales teams, and senior management eager to harness these technologies.\\nUnderstanding Machine Learning and Its Importance\\nMachine learning entails developing algorithms that empower computers to learn from data and predict outcomes. It automates analytical model building, vital in finance, healthcare, and e-commerce. Machine learning is categorized into three main types:\\nSupervised Learning:\\nDescription: Trains models on labeled data where inputs correspond to known outputs.\\nApplications: Spam detection, image classification, and predictive analytics.\\nUnsupervised Learning:\\nDescription: Models find patterns in data without labeled responses.\\nApplications: Clustering, anomaly detection, and market basket analysis.\\nReinforcement Learning:\\nDescription: Algorithms learn through rewards for correct actions and penalties for mistakes.\\nApplications: Robotics, gaming, and resource management.\\nExercise: Explore a cloud-based machine learning platform and create a simple model using sample data.\\nCore Principles of Machine Learning\\nThe essence of machine learning lies in algorithms, training, and models. Algorithms guide data learning, while training adjusts them for improvement. Models are the outcomes that make decisions.\\nSupervised vs. Unsupervised vs. Reinforcement Learning:\\nSupervised: Direct data approach with known answers.\\nUnsupervised: Exploratory data analysis.\\nReinforcement: Interaction-based learning with environments.\\nExercise: Create a flowchart illustrating the machine learning process from data input to output.\\nMachine Learning in Cloud Computing: Enhancing Scalability\\nCloud computing enhances machine learning by providing on-demand, scalable resources. It enables organizations to handle extensive datasets and deploy models without massive infrastructure investments.\\nAdvantages of Cloud-based Machine Learning\\nScalability: Adapt resources according to demand.\\nCost Efficiency: Pay-as-you-go, minimizing hardware investments.\\nAccessibility: Global data and application access.\\nIntegration: Seamless data source and service integration.\\nBest Practices\\nEnsure data quality before training models.\\nRegularly refresh models with new data.\\nPitfalls\\nNeglecting data preprocessing can degrade model performance.\\nOverfitting limits model generality in real-world scenarios.\\nPredictive Analytics with Machine Learning\\nPredictive analytics leverages machine learning for future event forecasting, significant in industries like finance and retail.\\nApplications of Predictive Analytics\\nMachine learning unveils patterns invisible to human analysis, making it ideal for predictive forecasting. For instance, retail uses it to anticipate customer behavior and manage inventory efficiently.\\nCase Study\\nA retailer employed machine learning to analyze purchasing data, predicting customer demands and optimizing stock levels, reducing both overstock and understock situations.\\nAutomation and Machine Learning: Streamlining Processes\\nMachine learning strengthens automation, enabling complex task execution without human intervention.\\nAutomated Systems with Machine Learning\\nMachine learning powers tasks like fraud detection, chatbot operations, and patient record analysis, reducing errors and freeing up human resources for critical tasks.\\nCase Study\\nA healthcare provider automated patient record analysis with machine learning, improving diagnosis speed and accuracy and allowing medical staff to focus on patient care.\\nImplementation Considerations for Machine Learning in the Cloud\\nImplementing machine learning in the cloud requires careful consideration of architectural components like data storage and processing capabilities.\\nKey Architectural Considerations\\nData Storage: Choose storage solutions based on data types and access needs.\\nProcessing Power: Ensure adequate processing for training and inference.\\nDevOps Integration: Employ CI\\/CD processes for timely model updates and deployments.\\nBest Practices\\nRefresh models with new, relevant data.\\nPitfalls\\nOverlooking model evaluation and tuning can lead to stagnation.\\nConclusion: The Future of Machine Learning and Cloud Computing\\nMachine learning integrated with cloud computing promises innovation and efficiency, empowering businesses with data-driven insights and process automation.\\nEncourage further exploration of machine learning tools and resources.\\nVisual Aid Suggestions\\nCloud Architecture Diagram: Show data flow, processing, and storage components for machine learning applications.\\nTool Screenshots: Briefly explain features of platforms like Google AI Platform and AWS SageMaker.\\nKey Takeaways\\nMachine learning drives automation and forecasting in technology.\\nCloud computing offers flexible, cost-effective deployment for machine learning.\\nUnderstanding machine learning types is essential for appropriate application.\\nEffective deployment requires attention to data, model maintenance, and DevOps integration.\\nGlossary\\nMachine Learning: Algorithms enabling learning and prediction based on data.\\nPredictive Analytics: Uses data to predict potential future events.\\nAutomation: Technology performing tasks independently.\\nCloud Computing: Internet-based computing services delivery.\\nDevOps: Combines development and operations for lifecycle efficiency.\\nKnowledge Check\\nWhat is the difference between supervised and unsupervised learning? (MCQ)\\nExplain how cloud computing impacts the deployment of machine learning models. (Short Answer)\\nFurther Reading\\nMachine Learning in the Cloud: What You Need to Know\\nML Best Practices\\nAWS: What is Machine Learning?\\nPost Views:\\n680\\nLeave a Reply\\nCancel Reply\\nYour email address will not be published.\\nRequired fields are marked\\n*\\nName\\n\\u00a0*\\nEmail\\n\\u00a0*\\nWebsite\\nAdd Comment\\n\\u00a0*\\nSave my name, email and website in this browser for the next time I comment.\\nPost Comment\\nTrending now\\nTrilogy Introduces Manuscript AI: Revolutionizing Manuscript Assessment with Artificial Intelligence\\nAdvanced AI Model Evaluation Techniques for Scalable Cloud Deployment\\nDesigning Autonomous AI Agents for Cloud Workflow Automation\\nDesigning Autonomous AI Agents for Cloud Workflow Automation\\nAI Advanced Articles\\nAI Tools Articles\\nTop YouTube Channels to Master AI\\nTriple C Engineering\\nLearn AI Articles\\nAI News\\nAbout Us\\nAbout Organization\\nSupport & Services\\nCompany Main Website\\n\\u00a0\\n\"}"
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"content": "{\"data\":\"Skip to content\\nNo results\\nAI Advanced Articles\\nAI Tools Articles\\nTop YouTube Channels to Master AI\\nTriple C Engineering\\nLearn AI Articles\\nAI News\\nPhysical Address\\n304 North Cardinal St.\\nDorchester Center, MA 02124\\nTriple C Engineering\\nLearn AI Articles\\nAI Tools Articles\\nAI Advanced Articles\\nAI News\\nTop YouTube Channels to Master AI\\nSearch\\nMenu\\nUnderstanding Machine Learning and Its Cloud Applications\\nwpai\\nJune 12, 2025\\nLearn AI Articles\\nMachine Learning in Cloud Computing: An In-Depth Exploration\\nMeta Summary: Discover the synergy between machine learning and cloud computing, a powerful combination reshaping industries through scalable insights, automation, and predictive analytics.\\nIntroduction to Machine Learning\\nMachine learning, a transformative arm of artificial intelligence, forms the backbone of numerous technological breakthroughs. Its fusion with cloud computing has unlocked scalable resources, revolutionizing data analysis and processing. This in-depth article targets technical professionals, sales teams, and senior management eager to harness these technologies.\\nUnderstanding Machine Learning and Its Importance\\nMachine learning entails developing algorithms that empower computers to learn from data and predict outcomes. It automates analytical model building, vital in finance, healthcare, and e-commerce. Machine learning is categorized into three main types:\\nSupervised Learning:\\nDescription: Trains models on labeled data where inputs correspond to known outputs.\\nApplications: Spam detection, image classification, and predictive analytics.\\nUnsupervised Learning:\\nDescription: Models find patterns in data without labeled responses.\\nApplications: Clustering, anomaly detection, and market basket analysis.\\nReinforcement Learning:\\nDescription: Algorithms learn through rewards for correct actions and penalties for mistakes.\\nApplications: Robotics, gaming, and resource management.\\nExercise: Explore a cloud-based machine learning platform and create a simple model using sample data.\\nCore Principles of Machine Learning\\nThe essence of machine learning lies in algorithms, training, and models. Algorithms guide data learning, while training adjusts them for improvement. Models are the outcomes that make decisions.\\nSupervised vs. Unsupervised vs. Reinforcement Learning:\\nSupervised: Direct data approach with known answers.\\nUnsupervised: Exploratory data analysis.\\nReinforcement: Interaction-based learning with environments.\\nExercise: Create a flowchart illustrating the machine learning process from data input to output.\\nMachine Learning in Cloud Computing: Enhancing Scalability\\nCloud computing enhances machine learning by providing on-demand, scalable resources. It enables organizations to handle extensive datasets and deploy models without massive infrastructure investments.\\nAdvantages of Cloud-based Machine Learning\\nScalability: Adapt resources according to demand.\\nCost Efficiency: Pay-as-you-go, minimizing hardware investments.\\nAccessibility: Global data and application access.\\nIntegration: Seamless data source and service integration.\\nBest Practices\\nEnsure data quality before training models.\\nRegularly refresh models with new data.\\nPitfalls\\nNeglecting data preprocessing can degrade model performance.\\nOverfitting limits model generality in real-world scenarios.\\nPredictive Analytics with Machine Learning\\nPredictive analytics leverages machine learning for future event forecasting, significant in industries like finance and retail.\\nApplications of Predictive Analytics\\nMachine learning unveils patterns invisible to human analysis, making it ideal for predictive forecasting. For instance, retail uses it to anticipate customer behavior and manage inventory efficiently.\\nCase Study\\nA retailer employed machine learning to analyze purchasing data, predicting customer demands and optimizing stock levels, reducing both overstock and understock situations.\\nAutomation and Machine Learning: Streamlining Processes\\nMachine learning strengthens automation, enabling complex task execution without human intervention.\\nAutomated Systems with Machine Learning\\nMachine learning powers tasks like fraud detection, chatbot operations, and patient record analysis, reducing errors and freeing up human resources for critical tasks.\\nCase Study\\nA healthcare provider automated patient record analysis with machine learning, improving diagnosis speed and accuracy and allowing medical staff to focus on patient care.\\nImplementation Considerations for Machine Learning in the Cloud\\nImplementing machine learning in the cloud requires careful consideration of architectural components like data storage and processing capabilities.\\nKey Architectural Considerations\\nData Storage: Choose storage solutions based on data types and access needs.\\nProcessing Power: Ensure adequate processing for training and inference.\\nDevOps Integration: Employ CI\\/CD processes for timely model updates and deployments.\\nBest Practices\\nRefresh models with new, relevant data.\\nPitfalls\\nOverlooking model evaluation and tuning can lead to stagnation.\\nConclusion: The Future of Machine Learning and Cloud Computing\\nMachine learning integrated with cloud computing promises innovation and efficiency, empowering businesses with data-driven insights and process automation.\\nEncourage further exploration of machine learning tools and resources.\\nVisual Aid Suggestions\\nCloud Architecture Diagram: Show data flow, processing, and storage components for machine learning applications.\\nTool Screenshots: Briefly explain features of platforms like Google AI Platform and AWS SageMaker.\\nKey Takeaways\\nMachine learning drives automation and forecasting in technology.\\nCloud computing offers flexible, cost-effective deployment for machine learning.\\nUnderstanding machine learning types is essential for appropriate application.\\nEffective deployment requires attention to data, model maintenance, and DevOps integration.\\nGlossary\\nMachine Learning: Algorithms enabling learning and prediction based on data.\\nPredictive Analytics: Uses data to predict potential future events.\\nAutomation: Technology performing tasks independently.\\nCloud Computing: Internet-based computing services delivery.\\nDevOps: Combines development and operations for lifecycle efficiency.\\nKnowledge Check\\nWhat is the difference between supervised and unsupervised learning? (MCQ)\\nExplain how cloud computing impacts the deployment of machine learning models. (Short Answer)\\nFurther Reading\\nMachine Learning in the Cloud: What You Need to Know\\nML Best Practices\\nAWS: What is Machine Learning?\\nPost Views:\\n680\\nLeave a Reply\\nCancel Reply\\nYour email address will not be published.\\nRequired fields are marked\\n*\\nName\\n\\u00a0*\\nEmail\\n\\u00a0*\\nWebsite\\nAdd Comment\\n\\u00a0*\\nSave my name, email and website in this browser for the next time I comment.\\nPost Comment\\nTrending now\\nTrilogy Introduces Manuscript AI: Revolutionizing Manuscript Assessment with Artificial Intelligence\\nAdvanced AI Model Evaluation Techniques for Scalable Cloud Deployment\\nDesigning Autonomous AI Agents for Cloud Workflow Automation\\nDesigning Autonomous AI Agents for Cloud Workflow Automation\\nAI Advanced Articles\\nAI Tools Articles\\nTop YouTube Channels to Master AI\\nTriple C Engineering\\nLearn AI Articles\\nAI News\\nAbout Us\\nAbout Organization\\nSupport & Services\\nCompany Main Website\\n\\u00a0\\n\"}"
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"This article is about understanding machine learning and its applications in cloud computing.",
"Machine learning uses algorithms to learn from data and predict outcomes, divided into supervised, unsupervised, and reinforcement learning.",
"Cloud computing enhances machine learning by providing scalable resources and cost efficiency.",
"Predictive analytics uses machine learning to forecast future events, like in retail for managing inventory.",
"Machine learning strengthens automation in areas like fraud detection and patient record analysis.",
"Implementing machine learning in the cloud requires careful consideration of data storage, processing power, and DevOps integration.",
"Key takeaways include machine learning driving automation, cloud computing offering flexible deployment, and the importance of data and model maintenance.",
"The article was posted on June 12, 2025.",
"The article has 680 views."
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"content": "{\"data\":\"What we do\\nTechnology Consulting\\nDigital Transformation\\nCustom Solution Development\\nTeam Extension\\nAI-Augmented Development Services\\nSolutions\\nSoftware Product Engineering\\nApplication Modernization\\nSoftware QA & Testing\\nMobile App Development\\nUI\\/UX\\nEmbedded & IoT\\nCloud Solutions and Consulting\\nCloud Adoption\\nCloud Migration\\nDevOps\\nCloud Infrastructure Management\\nCloud Modernization\\nCloud Application Development\\nIntelligent Platforms and Automation\\nRPA\\nLow-Code\\/No-Code\\nERP Solutions\\nSAP\\nOpenText\\nImmersive Tech\\nData and Analytics\\nData Lake Consulting\\nData Warehouse Consulting Services\\nData Governance\\nData Migration\\nData Engineering Services\\nBusiness Intelligence\\nAI Consulting and Implementation\\nAI Agent Development Services\\nComputer Vision\\nGenerative AI\\nAI & Machine Learning\\nSecurity and Quality\\nBusiness Continuity\\nDevSecOps\\nApplication Security Testing\\nPenetration Testing\\nGame Development\\nIndustries\\nValue we deliver\\nWe deliver real value to clients because of our deep knowledge of the industries in which they operate.\\nIndustries we serve\\nFinance\\nRetail\\nHealthcare\\nManufacturing\\nTelecom\\nEnergy & Utilities\\nLogistics & Supply Chain\\nAutomotive\\nAgritech\\nGame Development\\nData sovereignty: What does compliance require in 2026?\\nAbout us\\nWhy N-iX\\nN-iX is a global software solutions and engineering services company that helps world\\u2019s leading organizations turn challenges into lasting business value.\\nCompany Overview\\nLeadership Team\\nIndustry Recognitions\\nPartnerships\\nClients\\nCareers\\nNews\\nEvents\\nSustainability\\nPress kit\\nN-iX achieves Premier Tier Services Status within the AWS Partner Network\\nSuccess stories\\nInsights\\nExpert Blogs\\nReports & Ebooks\\nWhitepapers\\nWebinars\\nTrending topics\\nAI\\nData Analytics\\nData Lakes & Warehouses\\nCloud\\nEmbedded & IoT\\nComputer Vision\\nCybersecurity\\nEnterprise Platforms\\nSoftware Engineering\\nIT Outsourcing\\nHow to implement data governance in healthcare: 7-step framework\\nContact us\\nHome\\nN-iX insights\\nHow Machine Learning in cloud computing solves real business challenges (2025 guide)\\nTable of contents\\nExpert blog\\nHow Machine Learning in cloud computing solves real business challenges (2025 guide)\\nBy\\n \\n Andrzej Bedychaj, Data Science Engineer\\nJune 23, 2025\\n\\u25a0\\n7 min read\\nSummarize:\\nMachine Learning in cloud computing has already fundamentally changed how businesses operate and strategize. According to PWC\\u2019s 2024 Cloud and AI Business Survey,\\n92% of companies that are already capitalizing on their ML investments plan to increase their cloud budgets\\n. The reason? Most responders unanimously agree that\\ncloud solutions\\nsupport their ability to maintain and expand ML initiatives.\\nCloud platforms provide the essential infrastructure that ML workloads demand, including scalable resources and powerful computing capabilities. Machine Learning, in turn, enables competitive data analysis, intelligent decision-making, and robust automation across industries.\\nWith cloud computing and Machine Learning fueling faster innovation and enhanced service, the main challenge is combining them effectively. In this guide, N-iX experts define the most important factors to get right when\\nimplementing ML\\nin the cloud.\\nRead on to discover practical tips and real-world use cases where these technologies deliver measurable results.\\nKey considerations for Machine Learning in cloud computing projects\\nUsing cloud computing for Machine Learning requires strategic planning in several critical areas. Make sure to consider the following aspects before launching your ML initiatives in the cloud:\\nManaged ML platforms vs custom pipelines\\nWhen building ML solutions in the cloud, enterprises often choose between managed ML platforms and custom-built pipelines. Managed platforms, such as AWS SageMaker and Google Vertex AI, offer integrated tools for the entire Machine Learning project lifecycle. These services provide pre-built environments, auto-scaling infrastructure, experiment tracking, and orchestration. If you\\u2019re looking to\\naccelerate time-to-value and reduce operational overhead\\n, managed platforms can be right for your needs.\\nIn contrast, custom ML pipelines are typically built using a combination of open-source tools (like MLflow, Airflow, or Kubeflow) and cloud-native services. They can be\\ntailored to specific requirements\\nthat managed platforms may not fully support. Custom ML pipelines also give organizations more flexibility and control.\\nWhen it comes to choosing the best approach, our experts recommend evaluating your organization\\u2019s needs and existing infrastructure.\\nIn our experience implementing both approaches, the decision between managed and custom services often evolves over time. Organizations frequently start with managed services to build momentum, then selectively customize components as their ML capabilities mature. The cloud gives you the flexibility to adopt this hybrid approach without committing to either extreme.\\nValentyn Kropov, Chief Technology Officer\\nN-iX\\non\\nN-iX\\nCost control strategies for long-running ML jobs\\nCost management is crucial for ML projects in the cloud, especially when dealing with extended training jobs. Here are several effective strategies you can implement to optimize your spending:\\nUsing Spot Instances\\nfor interruptible training jobs (\\nup to 90% savings\\n, according to AWS);\\nImplementing auto-scaling\\nfor model endpoints to optimize resource usage;\\nConfiguring automatic shutdown\\nfor idle compute instances;\\nMonitoring performance metrics\\nto identify and eliminate waste.\\nDiscover more\\ncloud cost optimization strategies and tips\\nMulti-cloud vs single-cloud vs hybrid cloud for ML workloads\\nSingle-cloud environments\\noffer simplicity and straightforward management, with all resources centralized on one platform. They typically enable deeper integration among services since all applications operate within the same ecosystem.\\nWhite paper\\nAWS, Azure, or GCP? Choose the best cloud for AI workloads!\\nFull name\\n*\\nBusiness Email\\n*\\nBy submitting my details I accept\\nTerms\\n & Conditions\\nto receive relevant news & marketing communication from N\\u2011iX and\\n I\\u2019m aware that I can unsubscribe at any time. For more information, please see our\\nPrivacy Policy\\n*\\nSend my copy\\nSuccess!\\nAt the same time,\\nmulti-cloud strategies\\ndistribute workloads across different providers, offering enhanced risk mitigation, resilience, and access to specialized ML tools. This approach is often used to avoid vendor lock-in or meet specific workload needs with the most suitable cloud services.\\nAnother option,\\nthe hybrid cloud\\n, combines public and private environments.\\nHybrid infrastructures\\nare commonly used when organizations need to meet strict regulatory requirements or keep sensitive data on-premises while running ML models in the cloud.\\nSo, how to determine which one is right for your business needs? If you comply with industry-specific security requirements or prioritize preventing vendor lock-in,\\nmulti-cloud and hybrid cloud\\nstrategies can be a good fit. At the same time, a single-cloud setup can also be a practical choice. It offers the fastest and most straightforward path forward, which is particularly useful for early-stage ML initiatives.\\nSecurity and compliance for ML data\\nChoosing the right cloud model is only one part of securing Machine Learning workloads. To meet growing regulatory demands and protect sensitive assets, organizations must secure both their infrastructure and data throughout the ML lifecycle.\\nWhen N-iX teams build environments for Machine Learning in cloud computing projects, our cloud security experts typically implement the following measures:\\nIsolating\\ncritical ML components within private cloud networks;\\nEncrypting\\ndata in transit and at rest;\\nApplying\\nstrict identity and access management strategies based on least privilege;\\nEnabling\\naudit logging and monitoring for key activities;\\nManaging\\nsecrets and credentials securely using vault services.\\nReal-world applications of Machine Learning in the cloud\\nMachine Learning and cloud computing deliver tangible business outcomes when applied to real-world challenges. But what exactly are they capable of? Let\\u2019s explore three projects where N-iX combined these technologies to solve specific issues across fintech, telecom, and ecommerce.\\nCase #1: Fraud detection and automated transaction handling in fintech\\nMachine Learning in cloud computing enables real-time fraud detection by processing millions of transactions instantly and identifying patterns invisible to human analysts. It also helps financial institutions automatically approve or decline payments, routing only high-risk cases for manual review.\\nOur client, a large financial services provider based in the UK, partnered with N-iX to\\nstreamline transaction processing and compliance with Anti-Money Laundering (AML) and anti-fraud regulations\\n. The company wanted to centralize decision-making while enabling instant payment, even for peak transaction volumes.\\nOur team built a cloud-agnostic ML solution that automatically evaluates each payment based on customer history, login behavior, IP address, and credit information. Key project goals included:\\nBuilding\\na Machine Learning Operations (MLOps) pipeline to streamline model deployment;\\nEstablishing\\nreal-time data processing to enable ML-powered decisions;\\nCreating\\na single system to manage and test decision-making logic;\\nConsolidating\\nthe client\\u2019s previously developed ML models into a single solution.\\nAfter the launch, the client was able to decrease transaction processing latency to 250 milliseconds from up to 5 minutes. Furthermore, they saw a\\n20% increase in the number of customers\\nand a 35-point rise in their Net Promoter Score (NPS), indicating a high level of customer satisfaction.\\nExplore the full case:\\nPreventing fraud and automating payment processing with ML\\nCase #2: Predictive equipment maintenance in telecom\\nUnnecessary downtime is a financial sinkhole for businesses. For instance, our client, a global in-flight Internet provider, struggled with unexpected equipment failure. Their satellite antennas sometimes malfunctioned without a clear cause, affecting service quality and triggering costly penalties from airlines.\\nTo address the issue, the N-iX team helped the client migrate to AWS and developed a unified cloud-based data platform. This allowed us to\\nimplement Machine Learning in the cloud\\nand\\nestablish real-time monitoring of antenna health\\n, leading to advanced failure prediction. Here\\u2019s what we implemented:\\nCloud migration\\nto enable the processing of up to 3 TB of daily streaming data from over 20 sources;\\nA unified data platform\\nto aggregate telemetry such as uptime and concurrent in-flight Wi-Fi sessions;\\nPredictive analytics models\\n, including the Gaussian Mixture Model and Regression Analysis for failure forecasting;\\nA data pipeline\\ncorrelating degraded antenna performance with weather conditions and equipment architecture;\\nA user experience monitoring tool\\nto detect pain points in the first 15 seconds of connection attempts.\\nThese capabilities enabled the client to identify the main cause of antenna degradation and prevent future failures. As a result, the client significantly reduced service disruptions and saved costs previously spent on their on-premises infrastructure.\\nRead the full case:\\nEnhancing maintenance and operation of in-flight Internet\\nCase #3: Churn prediction in ecommerce\\nCloud-based Machine Learning enables businesses to\\ndeliver personalized experiences\\n,\\npredict customer behavior\\n, and\\nautomate responses\\n. These capabilities are especially crucial in subscription-based ecommerce, where tailored engagement directly impacts customer retention.\\nOur client, a global provider of ecommerce and subscription management platforms, wanted to expand their email marketing feature with churn prediction and automated campaign targeting. Their goal was to segment users, predict cancellations, and reduce the manual workload involved in managing campaigns.\\nTo meet these needs, N-iX delivered a cloud-native ML-powered solution integrated into the client\\u2019s platform. Our team\\u2019s key steps included:\\nDeveloping a proof of concept (PoC) prototype\\nand designing the architecture to automate marketing workflows;\\nBuilding a serverless AWS-based solution\\nto cut infrastructure costs and enable seamless ML integration;\\nApplying MLOps best practices\\nto streamline the deployment and monitoring of campaign automation models;\\nEnabling multi-tenant ML capabilities\\nusing Amazon SageMaker to predict churn and email engagement rates.\\nBy successfully implementing Machine Learning in cloud computing, we helped the client automate targeted marketing based on user behavior, ultimately boosting their customer retention and acquisition.\\nFind the full case here:\\nPredicting cancellations in ecommerce with Machine Learning\\nWrapping up\\nMachine Learning in cloud computing has reshaped how organizations approach data processing decision-making. The real-world applications we\\u2019ve examined demonstrate tangible business impact across industries. Financial institutions combat fraud more effectively, telecom enterprises prevent costly downtime, and ecommerce vendors improve customer experience.\\nHowever, implementing cloud-based Machine Learning comes with its challenges. Organizations need thoughtful planning, appropriate platform selection, and expert guidance to navigate the evolving landscape effectively. The question is no longer whether to use the cloud for ML but how to optimize your strategy for maximum business impact.\\nWhy should you partner with N-iX for your cloud Machine Learning project?\\nSelecting the right partner for ML and cloud integration can determine the success of your digital initiatives. With\\n23 years\\nof proven experience in cloud development across manufacturing, logistics, fintech, and telecom industries, N-iX brings the expertise organizations need to succeed.\\nOur specialized\\ndata and ML team of 200 professionals\\noffers deep expertise in building AI strategies, implementing ML models, and deploying Machine Learning pipelines.\\nThe N-iX team includes more than\\n400 cloud experts\\nwho have completed over 150 cloud projects in the last five years, delivering reliable cloud-based AI and ML integration tailored to specific business requirements.\\nN-iX has earned distinction as one of the first companies worldwide to become a\\nSolutions Partner in the Microsoft AI Cloud Partner Program\\n.\\nWe have obtained\\nVisual Intelligence Expertise\\nas part of Google Cloud\\u2019s Partner Advantage program and achieved\\nPremier Tier Services Status\\nin the AWS Partner Network.\\nCertified in\\nISO 27001\\n,\\nISO 9001:2015\\n,\\nSOC 2\\n, and\\nPCI DSS\\n, N-iX ensures your data remains secure throughout the entire ML implementation process.\\nHave a question?\\nSpeak to an expert\\nSergii Netesanyi\\nHead of Solution Group\\nFull name\\n*\\nBusiness Email\\n*\\nMessage\\n*\\nRequired fields\\n*\\nI have read and accepted N-iX\\nPrivacy Policy\\n*\\nSend Message\\nTable of contents\\nRelated Expertise and Services\\nCloud Solutions\\nBig Data analytics for improved maintenance and flawless operation of the in-flight internet\\nAutomating transaction handling in finance with Machine Learning\\nWhite paper\\nAWS, Azure, or GCP? Choose the best cloud for AI workloads!\\nSend my copy\\nWhite paper\\nAWS, Azure, or GCP? Choose the best cloud for AI workloads!\\nFull name\\n*\\nBusiness Email\\n*\\nBy submitting my details I accept\\nTerms\\n & Conditions\\nto receive relevant news & marketing communication from N\\u2011iX and\\n I\\u2019m aware that I can unsubscribe at any time. For more information, please see our\\nPrivacy Policy\\n*\\nSend my copy\\nSuccess!\\nRelated Articles\\nExpert blog\\n25 November 2025\\nAzure vs AWS AI: Evaluating cloud tools and services for your AI needs\\nRead more\\nExpert blog\\n12 November 2025\\nCloud adoption in insurance: What drives the shift and how to navigate it\\nRead more\\nExpert blog\\n07 November 2025\\nHow MATLAB AI transforms engineering: Insights from MathWorks AI Day\\nRead more\\ncontact@n-ix.com\\nUSA office:\\n4330 W Broward Boulevard \\u2013 Space P\\/Q, Plantation, FL 33317\\n+17273415669\\nonline\\noffline\\nUK office:\\nLondon\\nEC3A 7BA, 6 Bevis Marks\\n+442037407669\\nonline\\noffline\\nCopyright \\u00a9 2002 - 2026 N-iX LLC\\nPrivacy Notice\\nTerms & Conditions\\nCookie Policy\\nChange Cookie Preferences\\nPress kit\\nWe use cookies to enhance your experience and improve our services.\\nPlease review our\\nCookie Policy\\nand\\nPrivacy Notice\\n.\\nBy clicking Agree, you accept our use of cookies and privacy practices.\\nAccept all cookies\\nAccept only essential cookies\\nCustomize\\nPrivacy Overview\\n\\u00d7\\nWe use\\u00a0strictly necessary, analytics and marketing cookies. Please find what\\n each category of cookie means, and cookie lists providing details about each of\\n them, in our\\nCookie Policy\\n. We\\n also use third-party cookies that help us analyze and understand how you use\\n this website.\\nStrictly necessary\\nAlways Enabled\\nThese cookies are necessary for the website to function and cannot be switched off in our systems. They are usually only set in response to actions made by you which amount to a request for services, such as setting your privacy preferences, logging in or filling in forms. These cookies do not store any personally identifiable information.\\nAnalytics\\nEnabled\\nAnalytics cookies allow us to count visits and traffic sources so we can measure and improve the performance of our website. All information these cookies collect is aggregated and therefore anonymous. If you do not allow these cookies we will not know when you have visited our website and will not be able to monitor its performance.\\nMarketing\\nEnabled\\nWe use marketing cookies to deliver many types of targeted digital marketing. We do this in order to provide you with a better user experience and to maintain, operate and continually improve our website. The cookie stores user data and behavior information, which allows advertising services to target audience according to variables.\\nAccept All Cookies\\nSave My Preferences\\nReject All Non-Essential Cookies\\n\"}"
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According to PWC\\u2019s 2024 Cloud and AI Business Survey,\\n92% of companies that are already capitalizing on their ML investments plan to increase their cloud budgets\\n. The reason? Most responders unanimously agree that\\ncloud solutions\\nsupport their ability to maintain and expand ML initiatives.\\nCloud platforms provide the essential infrastructure that ML workloads demand, including scalable resources and powerful computing capabilities. Machine Learning, in turn, enables competitive data analysis, intelligent decision-making, and robust automation across industries.\\nWith cloud computing and Machine Learning fueling faster innovation and enhanced service, the main challenge is combining them effectively. In this guide, N-iX experts define the most important factors to get right when\\nimplementing ML\\nin the cloud.\\nRead on to discover practical tips and real-world use cases where these technologies deliver measurable results.\\nKey considerations for Machine Learning in cloud computing projects\\nUsing cloud computing for Machine Learning requires strategic planning in several critical areas. Make sure to consider the following aspects before launching your ML initiatives in the cloud:\\nManaged ML platforms vs custom pipelines\\nWhen building ML solutions in the cloud, enterprises often choose between managed ML platforms and custom-built pipelines. Managed platforms, such as AWS SageMaker and Google Vertex AI, offer integrated tools for the entire Machine Learning project lifecycle. These services provide pre-built environments, auto-scaling infrastructure, experiment tracking, and orchestration. If you\\u2019re looking to\\naccelerate time-to-value and reduce operational overhead\\n, managed platforms can be right for your needs.\\nIn contrast, custom ML pipelines are typically built using a combination of open-source tools (like MLflow, Airflow, or Kubeflow) and cloud-native services. They can be\\ntailored to specific requirements\\nthat managed platforms may not fully support. Custom ML pipelines also give organizations more flexibility and control.\\nWhen it comes to choosing the best approach, our experts recommend evaluating your organization\\u2019s needs and existing infrastructure.\\nIn our experience implementing both approaches, the decision between managed and custom services often evolves over time. Organizations frequently start with managed services to build momentum, then selectively customize components as their ML capabilities mature. The cloud gives you the flexibility to adopt this hybrid approach without committing to either extreme.\\nValentyn Kropov, Chief Technology Officer\\nN-iX\\non\\nN-iX\\nCost control strategies for long-running ML jobs\\nCost management is crucial for ML projects in the cloud, especially when dealing with extended training jobs. Here are several effective strategies you can implement to optimize your spending:\\nUsing Spot Instances\\nfor interruptible training jobs (\\nup to 90% savings\\n, according to AWS);\\nImplementing auto-scaling\\nfor model endpoints to optimize resource usage;\\nConfiguring automatic shutdown\\nfor idle compute instances;\\nMonitoring performance metrics\\nto identify and eliminate waste.\\nDiscover more\\ncloud cost optimization strategies and tips\\nMulti-cloud vs single-cloud vs hybrid cloud for ML workloads\\nSingle-cloud environments\\noffer simplicity and straightforward management, with all resources centralized on one platform. They typically enable deeper integration among services since all applications operate within the same ecosystem.\\nWhite paper\\nAWS, Azure, or GCP? Choose the best cloud for AI workloads!\\nFull name\\n*\\nBusiness Email\\n*\\nBy submitting my details I accept\\nTerms\\n & Conditions\\nto receive relevant news & marketing communication from N\\u2011iX and\\n I\\u2019m aware that I can unsubscribe at any time. For more information, please see our\\nPrivacy Policy\\n*\\nSend my copy\\nSuccess!\\nAt the same time,\\nmulti-cloud strategies\\ndistribute workloads across different providers, offering enhanced risk mitigation, resilience, and access to specialized ML tools. This approach is often used to avoid vendor lock-in or meet specific workload needs with the most suitable cloud services.\\nAnother option,\\nthe hybrid cloud\\n, combines public and private environments.\\nHybrid infrastructures\\nare commonly used when organizations need to meet strict regulatory requirements or keep sensitive data on-premises while running ML models in the cloud.\\nSo, how to determine which one is right for your business needs? If you comply with industry-specific security requirements or prioritize preventing vendor lock-in,\\nmulti-cloud and hybrid cloud\\nstrategies can be a good fit. At the same time, a single-cloud setup can also be a practical choice. It offers the fastest and most straightforward path forward, which is particularly useful for early-stage ML initiatives.\\nSecurity and compliance for ML data\\nChoosing the right cloud model is only one part of securing Machine Learning workloads. To meet growing regulatory demands and protect sensitive assets, organizations must secure both their infrastructure and data throughout the ML lifecycle.\\nWhen N-iX teams build environments for Machine Learning in cloud computing projects, our cloud security experts typically implement the following measures:\\nIsolating\\ncritical ML components within private cloud networks;\\nEncrypting\\ndata in transit and at rest;\\nApplying\\nstrict identity and access management strategies based on least privilege;\\nEnabling\\naudit logging and monitoring for key activities;\\nManaging\\nsecrets and credentials securely using vault services.\\nReal-world applications of Machine Learning in the cloud\\nMachine Learning and cloud computing deliver tangible business outcomes when applied to real-world challenges. But what exactly are they capable of? Let\\u2019s explore three projects where N-iX combined these technologies to solve specific issues across fintech, telecom, and ecommerce.\\nCase #1: Fraud detection and automated transaction handling in fintech\\nMachine Learning in cloud computing enables real-time fraud detection by processing millions of transactions instantly and identifying patterns invisible to human analysts. It also helps financial institutions automatically approve or decline payments, routing only high-risk cases for manual review.\\nOur client, a large financial services provider based in the UK, partnered with N-iX to\\nstreamline transaction processing and compliance with Anti-Money Laundering (AML) and anti-fraud regulations\\n. The company wanted to centralize decision-making while enabling instant payment, even for peak transaction volumes.\\nOur team built a cloud-agnostic ML solution that automatically evaluates each payment based on customer history, login behavior, IP address, and credit information. Key project goals included:\\nBuilding\\na Machine Learning Operations (MLOps) pipeline to streamline model deployment;\\nEstablishing\\nreal-time data processing to enable ML-powered decisions;\\nCreating\\na single system to manage and test decision-making logic;\\nConsolidating\\nthe client\\u2019s previously developed ML models into a single solution.\\nAfter the launch, the client was able to decrease transaction processing latency to 250 milliseconds from up to 5 minutes. Furthermore, they saw a\\n20% increase in the number of customers\\nand a 35-point rise in their Net Promoter Score (NPS), indicating a high level of customer satisfaction.\\nExplore the full case:\\nPreventing fraud and automating payment processing with ML\\nCase #2: Predictive equipment maintenance in telecom\\nUnnecessary downtime is a financial sinkhole for businesses. For instance, our client, a global in-flight Internet provider, struggled with unexpected equipment failure. Their satellite antennas sometimes malfunctioned without a clear cause, affecting service quality and triggering costly penalties from airlines.\\nTo address the issue, the N-iX team helped the client migrate to AWS and developed a unified cloud-based data platform. This allowed us to\\nimplement Machine Learning in the cloud\\nand\\nestablish real-time monitoring of antenna health\\n, leading to advanced failure prediction. Here\\u2019s what we implemented:\\nCloud migration\\nto enable the processing of up to 3 TB of daily streaming data from over 20 sources;\\nA unified data platform\\nto aggregate telemetry such as uptime and concurrent in-flight Wi-Fi sessions;\\nPredictive analytics models\\n, including the Gaussian Mixture Model and Regression Analysis for failure forecasting;\\nA data pipeline\\ncorrelating degraded antenna performance with weather conditions and equipment architecture;\\nA user experience monitoring tool\\nto detect pain points in the first 15 seconds of connection attempts.\\nThese capabilities enabled the client to identify the main cause of antenna degradation and prevent future failures. As a result, the client significantly reduced service disruptions and saved costs previously spent on their on-premises infrastructure.\\nRead the full case:\\nEnhancing maintenance and operation of in-flight Internet\\nCase #3: Churn prediction in ecommerce\\nCloud-based Machine Learning enables businesses to\\ndeliver personalized experiences\\n,\\npredict customer behavior\\n, and\\nautomate responses\\n. These capabilities are especially crucial in subscription-based ecommerce, where tailored engagement directly impacts customer retention.\\nOur client, a global provider of ecommerce and subscription management platforms, wanted to expand their email marketing feature with churn prediction and automated campaign targeting. Their goal was to segment users, predict cancellations, and reduce the manual workload involved in managing campaigns.\\nTo meet these needs, N-iX delivered a cloud-native ML-powered solution integrated into the client\\u2019s platform. Our team\\u2019s key steps included:\\nDeveloping a proof of concept (PoC) prototype\\nand designing the architecture to automate marketing workflows;\\nBuilding a serverless AWS-based solution\\nto cut infrastructure costs and enable seamless ML integration;\\nApplying MLOps best practices\\nto streamline the deployment and monitoring of campaign automation models;\\nEnabling multi-tenant ML capabilities\\nusing Amazon SageMaker to predict churn and email engagement rates.\\nBy successfully implementing Machine Learning in cloud computing, we helped the client automate targeted marketing based on user behavior, ultimately boosting their customer retention and acquisition.\\nFind the full case here:\\nPredicting cancellations in ecommerce with Machine Learning\\nWrapping up\\nMachine Learning in cloud computing has reshaped how organizations approach data processing decision-making. The real-world applications we\\u2019ve examined demonstrate tangible business impact across industries. Financial institutions combat fraud more effectively, telecom enterprises prevent costly downtime, and ecommerce vendors improve customer experience.\\nHowever, implementing cloud-based Machine Learning comes with its challenges. Organizations need thoughtful planning, appropriate platform selection, and expert guidance to navigate the evolving landscape effectively. The question is no longer whether to use the cloud for ML but how to optimize your strategy for maximum business impact.\\nWhy should you partner with N-iX for your cloud Machine Learning project?\\nSelecting the right partner for ML and cloud integration can determine the success of your digital initiatives. With\\n23 years\\nof proven experience in cloud development across manufacturing, logistics, fintech, and telecom industries, N-iX brings the expertise organizations need to succeed.\\nOur specialized\\ndata and ML team of 200 professionals\\noffers deep expertise in building AI strategies, implementing ML models, and deploying Machine Learning pipelines.\\nThe N-iX team includes more than\\n400 cloud experts\\nwho have completed over 150 cloud projects in the last five years, delivering reliable cloud-based AI and ML integration tailored to specific business requirements.\\nN-iX has earned distinction as one of the first companies worldwide to become a\\nSolutions Partner in the Microsoft AI Cloud Partner Program\\n.\\nWe have obtained\\nVisual Intelligence Expertise\\nas part of Google Cloud\\u2019s Partner Advantage program and achieved\\nPremier Tier Services Status\\nin the AWS Partner Network.\\nCertified in\\nISO 27001\\n,\\nISO 9001:2015\\n,\\nSOC 2\\n, and\\nPCI DSS\\n, N-iX ensures your data remains secure throughout the entire ML implementation process.\\nHave a question?\\nSpeak to an expert\\nSergii Netesanyi\\nHead of Solution Group\\nFull name\\n*\\nBusiness Email\\n*\\nMessage\\n*\\nRequired fields\\n*\\nI have read and accepted N-iX\\nPrivacy Policy\\n*\\nSend Message\\nTable of contents\\nRelated Expertise and Services\\nCloud Solutions\\nBig Data analytics for improved maintenance and flawless operation of the in-flight internet\\nAutomating transaction handling in finance with Machine Learning\\nWhite paper\\nAWS, Azure, or GCP? Choose the best cloud for AI workloads!\\nSend my copy\\nWhite paper\\nAWS, Azure, or GCP? Choose the best cloud for AI workloads!\\nFull name\\n*\\nBusiness Email\\n*\\nBy submitting my details I accept\\nTerms\\n & Conditions\\nto receive relevant news & marketing communication from N\\u2011iX and\\n I\\u2019m aware that I can unsubscribe at any time. For more information, please see our\\nPrivacy Policy\\n*\\nSend my copy\\nSuccess!\\nRelated Articles\\nExpert blog\\n25 November 2025\\nAzure vs AWS AI: Evaluating cloud tools and services for your AI needs\\nRead more\\nExpert blog\\n12 November 2025\\nCloud adoption in insurance: What drives the shift and how to navigate it\\nRead more\\nExpert blog\\n07 November 2025\\nHow MATLAB AI transforms engineering: Insights from MathWorks AI Day\\nRead more\\ncontact@n-ix.com\\nUSA office:\\n4330 W Broward Boulevard \\u2013 Space P\\/Q, Plantation, FL 33317\\n+17273415669\\nonline\\noffline\\nUK office:\\nLondon\\nEC3A 7BA, 6 Bevis Marks\\n+442037407669\\nonline\\noffline\\nCopyright \\u00a9 2002 - 2026 N-iX LLC\\nPrivacy Notice\\nTerms & Conditions\\nCookie Policy\\nChange Cookie Preferences\\nPress kit\\nWe use cookies to enhance your experience and improve our services.\\nPlease review our\\nCookie Policy\\nand\\nPrivacy Notice\\n.\\nBy clicking Agree, you accept our use of cookies and privacy practices.\\nAccept all cookies\\nAccept only essential cookies\\nCustomize\\nPrivacy Overview\\n\\u00d7\\nWe use\\u00a0strictly necessary, analytics and marketing cookies. Please find what\\n each category of cookie means, and cookie lists providing details about each of\\n them, in our\\nCookie Policy\\n. We\\n also use third-party cookies that help us analyze and understand how you use\\n this website.\\nStrictly necessary\\nAlways Enabled\\nThese cookies are necessary for the website to function and cannot be switched off in our systems. They are usually only set in response to actions made by you which amount to a request for services, such as setting your privacy preferences, logging in or filling in forms. These cookies do not store any personally identifiable information.\\nAnalytics\\nEnabled\\nAnalytics cookies allow us to count visits and traffic sources so we can measure and improve the performance of our website. All information these cookies collect is aggregated and therefore anonymous. If you do not allow these cookies we will not know when you have visited our website and will not be able to monitor its performance.\\nMarketing\\nEnabled\\nWe use marketing cookies to deliver many types of targeted digital marketing. We do this in order to provide you with a better user experience and to maintain, operate and continually improve our website. The cookie stores user data and behavior information, which allows advertising services to target audience according to variables.\\nAccept All Cookies\\nSave My Preferences\\nReject All Non-Essential Cookies\\n\"}"
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"They offer services like:",
" * Tech consulting",
" * Digital transformation",
" * Software development",
" * AI solutions",
"They work with:",
" * Finance",
" * Retail",
" * Healthcare",
" * and other industries",
"N-iX has partnerships with AWS, Microsoft, and Google.",
"They have security certifications like ISO 27001 and SOC 2.",
"Machine learning (ML) in the cloud is changing how businesses work.",
"92% of companies using ML plan to increase cloud spending (PWC, 2024).",
"Cloud provides the infrastructure for ML, while ML helps with data analysis and automation.",
"Key considerations for using ML in the cloud:",
" * Choose between managed ML platforms (like AWS SageMaker) and custom pipelines.",
" * Control costs by using spot instances (AWS says up to 90% savings), auto-scaling, and monitoring.",
" * Decide on multi-cloud, single-cloud, or hybrid cloud based on needs.",
" * Secure data with encryption, access management, and monitoring.",
"Examples of ML in the cloud:",
" * Fraud detection: A UK finance company reduced transaction time from 5 minutes to 250 milliseconds, increased customers by 20%, and raised their Net Promoter Score by 35 points.",
" * Predictive maintenance: An in-flight internet provider prevented equipment failures by using real-time monitoring.",
" * Churn prediction: An e-commerce platform automated marketing to improve customer retention.",
"N-iX has 23 years of experience in cloud development.",
"They have a team of 200 data and ML experts, and 400 cloud experts.",
"N-iX has completed over 150 cloud projects in the last five years."
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"content": "{\"data\":\"Open menu\\nWebmail\\nNotifications\\nBack to menu\\nNo pending order\\nNo pending ticket\\nMy customer account\\nBack to menu\\nMy customer account\\nMy bills\\nMy products and services\\nMy payment methods\\nMy orders\\nMy contacts\\nMy support tickets\\nContact Sales\\nSupport\\nBack to menu\\nHelp center\\nSupport Levels\\nProfessional Services\\nCommunities\\nBack to menu\\nOVHcloud Community\\nOVHcloud Blog\\nEvents\\nLearn\\nBack to menu\\nDocumentation\\nTutorials\\nUse cases\\nDeveloper Centre\\nCompliance\\nCase Studies\\nVideos\\nStories\\nTraining\\nUnited Kingdom [\\u00a3]\\nBack to menu\\nEurope\\nDeutschland [\\u20ac]\\nEspa\\u00f1a [\\u20ac]\\nFrance [\\u20ac]\\nIreland [\\u20ac]\\nItalia [\\u20ac]\\nNederland [\\u20ac]\\nPolska [PLN]\\nPortugal [\\u20ac]\\nUnited Kingdom [\\u00a3]\\nAmerica\\nCanada (en) [$]\\nCanada (fr) [$]\\nUnited States [$]\\nAm\\u00e9rica Latina [$]\\nAfrica\\nMaroc [Dhs]\\nS\\u00e9n\\u00e9gal [FCFA]\\nTunisie 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needs\\nDedicated server availability by region\\nThe availability of our servers in various regions around the world\\nBare Metal Wholesale\\nGet a full rack of Advance, Scale or High Grade servers.\\nUse Cases\\nAZ and resilience\\nGrid computing\\nSAP HANA\\nVirtualisation and containerisation\\nWebsite Business Application\\nHyperconverged Infrastructure\\nSoftware-defined Storage\\nBig Data and Analytics\\nArchiving and Backup\\nAI, Machine Learning & Deep Learning\\nConfidential Computing\\nDatabase\\nGaming\\nHigh Performance Computing\\nEco Dedicated Servers\\nBack to menu\\nEco Dedicated Servers\\nEco Dedicated Servers\\nExplore our Dedicated Servers\\nKimsufi Servers\\nLow-cost servers to get you started\\nSo you Start Servers\\nA range of dedicated servers that are perfect for startups and very small businesses\\nRise Servers\\nNew\\nTried-and-tested Intel and AMD platforms for competitive performance and price\\nOperating systems and distributions\\nFind the versions compatible with your Eco server\\nUse cases\\nWebsite and business application\\nEmail server\\nNew\\nData storage\\nNew\\nVirtual private servers \\u2013 VPS\\nBack to menu\\nVirtual private servers \\u2013 VPS\\nVirtual private servers \\u2013 VPS\\nVPS\\nNew\\nOur new VPS with maximum resources, the best prices, instant scalability, and reinforced security, available in all our regions.\\nDistributions and licences\\nSee all our distributions and licences available on VPS\\nOptions\\nCustomise your VPS with our advanced options\\nHelp\\nUse cases\\nAutomated workflows with n8n\\nNew\\nWordPress multisite platform\\nVideo game server\\nTest server with VPS\\nHosting trading applications on a Forex VPS\\nManaged Bare Metal\\nBack to menu\\nManaged Bare Metal\\nManaged Bare Metal\\nManaged Bare Metal Essentials powered by VMware\\u00ae\\nYour virtual infrastructure managed by OVHcloud\\nStorage and backups\\nBack to menu\\nStorage and backups\\nStorage and backup\\nExplore all our solutions\\nEnterprise File Storage\\nFully managed file storage based on NetApp ONTAP Select\\nHA-NAS\\nCentralised storage or backup space for your file data\\nCloud Disk Array\\nA scalable storage solution based on CEPH technology\\nVeeam Enterprise Plus\\nThe ultimate solution for protecting your data in a way that fits your needs\\nHYCU for OVHcloud\\nSimplify the backup and migration of your Nutanix workloads\\nUse cases\\nData storage for your Linux servers\\nData storage for your virtual machines\\nNetwork\\nBack to menu\\nNetwork\\nNetwork\\nAdditional IP\\nAssign and move dynamic IP addresses from one service to another\\nOVHcloud Load Balancer\\nBalance your application load across multiple backend servers\\nPrivate Network (vRack)\\nConnect all your OVHcloud services together with an isolated private network\\nOVHcloud Link Aggregation\\nGet a redundant, high-bandwith private network\\nOVHcloud Connect\\nConnect your datacentre to OVHcloud\\nPublic Bandwith\\nUpgrade your default guaranteed bandwidth\\nCDN Infrastructure\\nA dedicated CDN to complement your OVHcloud products\\nBring Your Own IP (BYOIP)\\nBring your IP addresses and simplify your migration to OVHcloud\\nNetwork Security\\nBack to menu\\nNetwork Security\\nNetwork Security\\nAnti DDoS infrastructure\\nKeep your infrastructures protected against DDoS attacks\\nGame DDoS Protection\\nProtect your gaming and e-sport business with best-in-class security solution\\nDNSSEC\\nProtect your data from DNS cache poisoning\\nSSL Gateway\\nSimplest way to enable security on your webpage. Without any effort!\\nIdentity, Security & Operations\\nBack to menu\\nIdentity, Security & Operations\\nIdentity, Security & Operations\\nIdentity and Access Management (IAM)\\nSecure your access management and enhance your productivity\\nLogs Data Platform\\nComplete platform to collect, store and view your logs\\nKey Management Service (KMS)\\nSecure your data across all your OVHcloud services from one central place\\nSecret Manager\\nProfessionally manage of all your secrets in one place\\nService Logs\\nMonitor your cloud environment\\u2019s performance and security\\nBare Metal Pod\\nBack to menu\\nBare Metal Pod\\nBare Metal Pod\\nSecNumCloud-qualified Bare Metal Pod\\nBare Metal performance in a SecNumCloud compliant sovereign environment.\\nUse Cases\\nData sovereignty\\nSecNumCloud-qualified solutions\\nDomain Hosting Email\\nBack to menu\\nDomain Hosting Email\\nDomain name\\nBack to menu\\nDomain name\\nDomain name\\nSearch for your domain name\\nTransfer your domain name to OVHcloud\\nRenew your domain name\\nSearch the secondary market (aftermarket)\\nQuick access\\nDomain name prices\\nWHOIS - Search for domain information\\nDNS server\\nSpecial offers\\nNew\\nWeb Hosting\\nBack to menu\\nWeb Hosting\\nWeb Hosting\\nAll Web packs\\nAdditional databases\\nSSL option\\nCDN option\\nQuick access\\nHow do I create a website?\\nHost your WordPress website\\nCreate your website in 1 click\\nCreate your online store\\nEmail & collaborative solutions\\nBack to menu\\nEmail & collaborative solutions\\nEmail & collaborative solutions\\nAll Email plans\\nAll Exchange plans\\nOffice 365 licences\\nSMS\\nBack to menu\\nSMS\\nSMS\\nSend your Pro text messages\\nPublic Cloud\\nBack to menu\\nPublic Cloud\\nPublic Cloud\\nBack to menu\\nPublic Cloud\\nPublic Cloud\\nDiscover Public Cloud\\nService ecosystem\\nAutomate your setup, propel your business\\nPricing\\nView our simple, accessible pricing\\nPublic Cloud free trial\\nTry out our solutions at no cost\\nSavings Plans\\nNew\\nOpt for a 1 to 36 month subscription and save on your Public Cloud resources.\\nThe advantages of the Public Cloud solution\\nDiscover cloud computing solutions to meet your needs\\nCloud Computing\\nFind out more about this growing practice\\nCertification & compliance\\nExplore how OVHcloud builds trusted solutions\\nTrusted Zones\\nDeploy your critical data in high-security zones\\nAvailability by region\\nExplore our catalogue coverage by region\\nLocal Zones\\nNew\\nDeploy cloud services closer to your users\\nOur documentation\\nRead our guides and tutorials\\nCompute\\nBack to menu\\nCompute\\nCompute\\nExplore all our Compute products\\nVirtual Machine Instances\\nGet versatile instances that can be adapted to suit all your needs.\\nCloud GPU\\nSpeed up your workloads with high-performance GPU instances.\\nMetal Instances\\nCombine the power of Bare Metal with cloud automation\\nOur documentation\\nBrowse our documentation for the Compute range\\nEn route to PaaS\\nFocus on your applications & become more competitive\\nLocal Zone\\nNew\\nDeploy cloud services closer to your users\\nStorage\\nBack to menu\\nStorage\\nStorage\\nExplore all our Storage products\\nBlock Storage\\nCreate storage volumes, which can be used as additional disks\\nObject Storage\\nGet S3-compatible unlimited storage on demand\\nCold Archive\\nHighly economical archiving for infrequently accessed data.\\nLocal Zone\\nNew\\nDeploy cloud services closer to your users\\nOur documentation\\nBrowse our documentation for the Storage range\\nNetwork\\nBack to menu\\nNetwork\\nNetwork\\nExplore all our Network products\\nPrivate Network\\nDeploy private networks via the OVHcloud vRack\\nLoad Balancer\\nManage variable traffic by spreading it across multiple resources\\nFloating IP\\nAssign and move your public IP from one service to another\\nGateway\\nManage a single connection point between your private network & internet\\nOur documentation\\nView our documentation for the Network range\\nContainers & Orchestration\\nBack to menu\\nContainers & Orchestration\\nContainers & Orchestration\\nExplore all our Containers & Orchestration products\\nManaged Kubernetes Service\\nOrchestrate your containerised applications with a CNCF-certified Kubernetes cluster\\nLoad Balancer for Managed Kubernetes Service\\nManage variations in activity by distributing traffic across multiple resources\\nManaged Rancher Service\\nNew\\nSimplified and centralised management of your Kubernetes clusters\\nManaged Private Registry\\nManage your images of containers and helmchart on a secure private registry\\nOur documentation\\nView our documentation for the Containers & Orchestration range\\nEn route to PaaS\\nFocus on your applications & become more competitive\\nDatabases\\nBack to menu\\nDatabases\\nDatabases\\nExplore all our Databases products\\nMongoDB\\nDocument-oriented NoSQL engine. Try for free with Free Tier\\nMySQL\\nPopular relational database that adapts to your usage\\nPostgreSQL\\nThe top open-source relational database engine\\nValkey\\nSmart in-memory storage\\nOur Documentation\\nView our documentation for the Databases range\\nEn route to PaaS\\nFocus on your applications & become more competitive\\nAnalytics\\nBack to menu\\nAnalytics\\nAnalytics\\nExplore all our Analytics products\\nKafka\\nQueueing solution for deploying your event-driven architectures\\nKafka Connect\\nExtension simplifying the ingestion of your sources to Apache Kafka\\nKafka MirrorMaker\\nReplication ensuring high availability for your Kafka clusters\\nLogs Data Platform\\nComplete platform to collect, store and view your logs\\nOpenSearch\\nA dedicated engine for indexing, searching and analyzing data\\nClickHouse\\nNew\\nUltra-fast data analysis at your fingertips\\nManaged Dashboards\\nGrafana platform for creating dashboards\\nOur documentation\\nView our documentation for the Analytics range\\nEn route to PaaS\\nFocus on your applications & become more competitive\\nData Platform\\nNew\\nBack to menu\\nData Platform\\nData Platform\\nBrowse all our Data Platform products\\nDiscover the OVHcloud Data Platform\\nNew\\nCarry out your Data & Analytics projects in record time and with ease\\nData Catalog\\nNew\\nMore than 50 connectors for all your data sources\\nLakehouse Manager\\nNew\\nUnified data warehouse and data lake storage, based on Apache Iceberg\\nData Processing Engine\\nNew\\nAutomate the execution and orchestration of your ETL\\/ELT workloads\\nAnalytics Manager\\nNew\\nCreate your dashboards and launch your requests with the Trino engine\\nApplication Services\\nNew\\nSDK and serverless services to deploy your APIs and Data applications\\nControl Center\\nNew\\nMonitor metrics, manage logs and alerts for your environments\\nAI & Machine Learning\\nBack to menu\\nAI & Machine Learning\\nAI & Machine Learning\\nExplore all our AI & Machine learning products\\nAI & Quantum Notebooks\\nLaunch your Jupyter or VS Code notebooks in the cloud, and choose from our native AI or quantum frameworks\\nAI Training\\nTrain your AI models\\nAI Deploy\\nDeploy machine learning models & get your predictions\\nAI Endpoints\\nNew\\nIntegrate generative AI models into your applications easily and securely via standard APIs.\\nOur documentation\\nBrowse our documentation for the AI & Machine Learning range\\nEn route to PaaS\\nFocus on your applications & become more competitive\\nQuantum Computing\\nBack to menu\\nQuantum Computing\\nQuantum Computing\\nExplore all our Quantum Computing products\\nQuantum Emulators\\nNew\\nSimulate your quantum algorithms on ready-to-use notebooks\\nQuantum Processing Units (QPU)\\nNew\\nAccess real quantum computers through our Quantum Platform\\nWhat is Quantum computing?\\nLearn more about the next accelerated computing revolution and how to develop today\\u2019s solutions with tomorrow\\u2019s quantum computers\\nIdentity, Security & Operations\\nBack to menu\\nIdentity, Security & Operations\\nIdentity, Security & Operations\\nDiscover all our Identity, Security & Operations products\\nIdentity and Access Management (IAM)\\nSecure your access management and improve your productivity\\nLogs Data Platform\\nComplete platform to collect, store and view your logs\\nKey Management Service (KMS)\\nSecure your data across all your OVHcloud services from one place\\nSecret Manager\\nProfessionally manage of all your secrets in one place\\nServices Logs\\nMonitor the performance and security of your cloud environment\\nHosted Private Cloud\\nBack to menu\\nHosted Private Cloud\\nVMware\\nBack to menu\\nVMware\\nVMware on OVHcloud\\nDiscover VMware on OVHcloud\\nPublic VCF as a Service\\nNew\\nShared and managed VMware solution, powered by VMware Cloud Foundation\\nManaged VMware vSphere solutions\\nManaged VMware solution for all enterprises\\nSecNumCloud-qualified Managed VMware 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Private Cloud\\nThe solution that facilitates your SAP deployments in a sovereign cloud\\nSolutions\\nSAP on OVHcloud\\nOn-Prem Cloud Platform\\nBack to menu\\nOn-Prem Cloud Platform\\nOn-Prem Cloud Platform\\nOn-Prem Cloud Platform (OPCP)\\nOPCP enables you to deploy your services on-premises or at the edge, with complete autonomy and security. Air-gapped by design.\\nInfra as a Service\\nCore infrastructure to build and operate your on-premises cloud\\nPlatform as a Service\\nCloud-native environments and ready-to-use services\\nCloud ready\\nLanding Zone to deploy and manage workloads easily\\nStorage & Backup\\nBack to menu\\nStorage & Backup\\nStorage and backup\\nDiscover all the storage solutions\\nVeeam option for VMware backup\\nThe Veeam Managed Backup solution for backing up your virtual machines\\nZerto option for your VMware Disaster Recovery Plan\\nMulti-site Disaster Recovery Plan solution for your VMware clusters\\nVeeam option for Public VCF as a Service\\nA dedicated Veeam Backup Replication solution for all your backups\\nVeeam Enterprise - Licences\\nA dedicated Veeam Backup Replication solution for all your backups\\nHYCU for OVHcloud\\nSimplify the backup and migration of your Nutanix workloads\\nObject Storage\\nGet S3-compatible unlimited storage on demand\\nCold Archive\\nGet long-term archiving at the best price\\nNetApp - Enterprise File Storage\\nFully managed file storage based on NetApp ONTAP Select\\nUse cases\\nBackup and Disaster Recovery\\nBusiness continuity\\nDisaster Recovery for Managed VMware vSphere\\nDisaster Recovery for Nutanix on OVHcloud\\nNetwork\\nBack to menu\\nNetwork\\nNetwork\\nAdditional IP\\nAssign and move dynamic IP addresses from one service to another\\nOVHcloud Load Balancer\\nBalance your application load across multiple backend servers\\nPrivate Network (vRack)\\nConnect all your OVHcloud services together with an isolated private network\\nOVHcloud Connect\\nConnect your datacentre to OVHcloud\\nCDN Infrastructure\\nA dedicated CDN to complement your OVHcloud products\\nBring Your Own IP (BYOIP)\\nBring your IP addresses and simplify your migration to OVHcloud\\nNetwork Security\\nBack to menu\\nNetwork Security\\nNetwork Security\\nAnti-DDoS infrastructure\\nKeep your infrastructures protected against DDoS attacks\\nDNSSEC\\nProtect your data from DNS cache poisoning\\nSSL Gateway\\nSimplest way to enable security on your webpage. Without any effort!\\nIdentity, Security & Operations\\nBack to menu\\nIdentity, Security & Operations\\nIdentity, Security & Operations\\nIdentity and Access Management (IAM)\\nSecure your access management and enhance your productivity\\nLogs Data Platform\\nComplete platform to collect, store and view your logs\\nKey Management Service (KMS)\\nSecure your data across all your OVHcloud services from one central place\\nSecret Manager\\nProfessionally manage of all your secrets in one place\\nService Logs\\nMonitor your cloud environment\\u2019s performance and security\\nCompliance and certifications\\nBack to menu\\nCompliance and certifications\\nCompliance and certifications\\nFull list of standards and regulations\\nGDPR\\nCompliance with the General Data Protection Regulation (EU) 2016\\/679\\nSecNumCloud\\nANSSI Security Visa Qualification\\nHDS and healthcare data hosting\\nHosting healthcare data in the UK and across Europe\\nHIPAA and HITECH\\nHosting healthcare data in the US\\nPCI DSS\\nFinancial data hosting\\nISO\\/IEC 27001, 27017 and 27018\\nInformation Security Management\\nISO\\/IEC 27701\\nManaging the security of personal data processing\\nISO 14001\\nManaging environmental impacts\\nISO 50001\\nEnergy management\\nSOC 1, 2 and 3\\nAICPA SSAE 16\\/ISAE 3402 type II attestation and reports\\nEBA and ACPR\\nCompliance for financial services operators in Europe\\nG-Cloud\\nPublic sector cloud service delivery in the UK\\nSolutions\\nBack to menu\\nSolutions\\nUse Cases\\nBack to menu\\nUse Cases\\nUse Cases\\nMigrate to the cloud\\nHybrid cloud & Multi Cloud\\nApp Modernization\\nCloud Native App\\nArtificial Intelligence\\nBig Data Analytics\\nData Management\\nHigh-Performance Workloads\\nLarge Data set storage\\nGrid Computing\\nMove to PaaS\\nBackup and disaster recovery\\nBusiness Continuity\\nTrusted Zone\\nSecNumCloud Environment\\nNetwork Protection\\nCloud Security\\nExtension & Migration Datacenter\\nDatacenter transformation\\nElevate your brand reputation\\nPrioritise financial stability\\nProtect your business against cyberthreats\\nIndustry\\nBack to menu\\nIndustry\\nIndustry\\nPublic Sector\\nA trusted solution for governments and authorities\\nHealthcare\\nThe trusted solution for the healthcare sector\\nFinancial Service\\nOur solutions for financial services operators\\nManufacturing\\nThe trusted cloud solution for European manufacturers\\nSoftware\\/Information Technology\\nSaaS and PaaS solutions from OVHcloud Partner Software Publishers\\nGaming\\nCloud solutions for companies and individuals in the gaming industry\\nBlockchain\\nFree up your blockchain projects with OVHcloud solutions\\nOrganisation Type\\nBack to menu\\nOrganisation Type\\nOrganisation Type\\nEnterprise\\nSolutions for enterprise-level digital transformation\\nSoftware publishers (SaaS\\/PaaS)\\nSaaS and PaaS solutions from OVHcloud Partner Software Publishers\\nSystem Integrator\\nSolutions for integrators, service providers and consulting firms\\nGovernment \\/ Administrations\\nTrusted solutions for governments and authorities\\nStartup\\nSupport solutions for start-ups\\nScaleup\\nSupport solutions for scale-ups\\nTechnology\\nBack to menu\\nTechnology\\nTechnology\\nVeeam\\nProtect your data with Veeam solutions offered by OVHcloud\\nVMware by Broadcom\\nVMware by Broadcom and OVHcloud solutions for all your projects\\nNutanix\\nAccelerate and simplify your hybrid multicloud journey with Nutanix on OVHcloud solution\\nHYCU\\nThe backup solution trusted by Nutanix users\\nSAP\\nOur SAP on OVHcloud solutions for SAP environments hosting in a sovereign cloud\\nNetApp\\nNetApp storage solutions with cost control and high performance\\nNvidia\\nNvidia GPU solutions for accelerating your innovation and AI projects\\nMongoDB\\nMongoDB solutions that simplify data management\\nOpenStack\\nOpenStack solutions integrated with OVHcloud for cloud infrastructure\\nIntel\\nExpert solutions powered by Intel\\u00ae Xeon\\u00ae that accelerate cloud processes\\nAMD\\nTop-of-the-line cloud solutions powered by AMD processors\\nHadoop Cloudera\\nYour 100% managed Cloudera solution with Claranet\\nEcosystem\\nBack to menu\\nEcosystem\\nEcosystem\\nDiscover the OVHcloud partner ecosystem\\nPartner Program\\nAn initiative dedicated to our reseller partners, integrators, administrators and consultants.\\nOpen Trusted Cloud\\nAn ecosystem of labelled SaaS and PaaS solutions, hosted in our open, reversible and reliable cloud\\nStartup Program\\nA support program for startups and scaleups to accelerate their growth\\nOVHcloud Labs\\nThe innovation zone to test our cutting-edge technologies before they are officially launched on the market\\nOur Ecosystem events\\nFind all the events dedicated to our partner ecosystem: webinars, conferences, and more\\nOVHcloud Ecosystem Awards\\nDiscover our OVHcloud Ecosystem awards that rewards our ecosystem leaders of the Year per category\\nTraining & Certification\\nDevelop your expertise with the training and certifications available to OVHcloud Partner Program members.\\nQuick Access\\nFind a partner\\nSign up to the OVHcloud Partner Program\\nSign up to the OVHcloud Startup Program\\nPrice comparison\\nPartner Portal\\nFAQ Partner Program\\nAbout\\nBack to menu\\nAbout\\nAbout\\nWho we are\\nNewsroom\\nGlobal infrastructure\\nOur datacenters\\nOur Local Zones\\nBackbone network\\nJoin the OVHcloud adventure\\nPatent Pledge\\nLegal\\nGDPR - Personal data protection\\nOur commitments\\nInnovation\\nSustainable cloud\\nTrusted cloud\\nInvestor Relations\\nEnvironmental Impact Tracker\\nSummit\\nOpen search bar\\nClose search bar\\nNo result\\nProducts\\nSolutions\\nPartners\\nDocumentation\\nArticles\\nSee all results\\nWhat is machine learning?\\nLearning Centre\\nWhat is machine learning?\\nWe generate more and more information every day, thanks to the multiplicity of technologies we use (smartphones, computers, tablets, connected devices, etc.). All of these devices generate a massive amount of data. An average person generates 1.7 MB of data per second in 2020. Big data is a huge source of information, stored in digital databases. But without adequate processing and an effective strategy, this mass would be a collection of problematic bytes to pile up. This is where machine learning comes in and makes the most of it.\\nWhat is machine learning?\\nThe first machine learning algorithms were developed in 1950. Machine learning, or machine learning, is both a technology and a science (Data Science) that allows a computer to perform a learning process without having been programmed for that purpose. This technique \\u2014 which is linked to artificial intelligence (AI) \\u2013 is designed to highlight patterns of statistical repetition, and derive statistical predictions from them. Data\\nmining\\n, which is the extraction of information from a large amount of data, serves as the raw material for machine learning to highlight patterns for statistical prediction. This is why big data (all of the data generated and stored) is an integral part of machine learning. The larger the set that reveals trends, the more accurate the predictions.\\nMore specifically, the learning algorithm applied enables the computer to refine its analysis and responses, based on empirical data from the associated database. Machine learning is a great learning model for businesses, because it allows them to harness the power of the data generated by their customers or activity. Artificial intelligence is thus a major challenge if they are to succeed.\\nThere are several types of learning that are classified according to existing data during the learning phase. If the response to the defined task is already known, the data is referred to as \\u2018labelled\\u2019. This is what is known as supervised learning. Depending on whether the data is discrete or continuous, classification or regression is used. If the learning takes place step by step, with a reward system in place for each task performed correctly, then it is known as reinforcement learning. The most common type of learning is unsupervised learning, which involves searching without labels. It aims to predict a result, without using known answers beforehand.\\nTwo approaches to machine learning\\nSupervised machine learning\\nSupervised machine learning is a type of machine learning where a model is trained on a labeled data set. This means that each example in the data set has an input (or characteristic) and a corresponding output (or label). The goal is to learn a function that, from input characteristics, correctly predicts output labels for new data.\\nThe basic process of supervised machine learning is as follows.\\nData Collection: Gather a data set with labeled samples.\\nData division: separate data into training and test sets.\\nTraining: Use the training assembly to learn a model that connects input characteristics to output labels.\\nValidation and testing: Evaluate the model performance on the test set to verify its accuracy and generalizability.\\nSupervised machine learning is used in several types of activity: for classification (e.g., determining a category like spam) or predicting a numerical value (e.g., estimating the price of a house based on its characteristics).\\nSupervised learning is used in many practical applications, such as speech recognition, fraud detection and referral systems.\\nMachine learning not supervised\\nUnsupervised machine learning is a type of machine learning where a model is trained on unlabeled data. Unlike supervised learning, there is no predefined exit. The goal is to find hidden structures or patterns in the data.\\nMain types of unsupervised learning:\\nClustering: dividing data into similarity-based groups or clusters (e.g. grouping customers with similar buying behaviors);\\nDimensionality reduction: Simplify data by reducing the number of characteristics while retaining most of the information (for example, the principal components method or PCA).\\nCommon examples of unsupervised machine learning use:\\nCustomer segmentation: identify groups of customers with similar behaviors or characteristics;\\nanomaly detection: identify unusual data that does not follow general behavior (e.g. detect fraudulent transactions).\\nUnsupervised learning is useful for exploring data and discovering patterns or relationships without the need for prior knowledge of expected labels or outcomes.\\nWhat is machine learning used for?\\nThe power and advantage of machine learning lies in its ability to process a huge volume of data that is impossible for the human brain to process. Industries that gather a high volume of data need a solution for processing it, and extracting information that can be used for decision-making. Predictive analysis of this data enables the computer to anticipate specific situations. This is what machine learning is all about. Let us consider the financial services sector, for example. Machine learning is used to detect fraud, illegal conduct and other elements that are key for financial institutions to work properly.\\nThe growing volume of transactional data we generate is also used by companies to target their customers based on their purchasing behaviour, by identifying repetitions. The websites and pages we visit also generate data that can be used by machine learning to set our preferences. It is therefore clear that this data processing technique, without the need for human intervention, is a major asset for companies wishing to take advantage of the mass of information available to them. It is unlikely that a human being would be able to make use of this data themselves, because the volume of data to process is just so high. Take the large companies owned by GAFAM, for example: the implementation of AI and machine learning in their processes has become a necessity, due to the large usable data stream that they generate.\\nWith data being generated in ever-increasing volumes, a growing number of companies will also need to integrate this technology into their structure in order to make use of the information available to them. Connected devices, for example, are becoming increasingly present in our daily lives. By 2019, more than 8 billion connected objects had entered our society, allowing us to collect more data on our rhythm of life, our consumption, our habits, based on our voice recognition. All of this represents a huge mass of critical data for companies, and machine learning helps us identify the elements that are relevant and useful. Without a doubt, there is a lot at stake here. Big data plays a vital part in the development of many technologies for modern society \\u2014 like facial recognition, self-driving cars, robotics, and smart home technology, for example. But to create this technology, companies must learn how to implement this asset in a suitable way. This technology isn\\u2019t just for AI-savvy development teams. Many companies are embarking on the adventure of machine learning by choosing turn-key solutions that are adapted to fit their objectives.\\nHow machine learning works\\nMachine learning works based on \\u201cexperience\\u201d. The computer retrieves a high volume of data, and uses it to analyse and predict situations. The goal of the process is for the machine to independently create an \\u201cinternal plan\\u201d, which it can use to identify the key elements that the user wants to target. It will need to experiment with different examples and tests in order to progress. This is why we talk about learning.\\nTo train itself and learn, the computer needs learning data. Data mining is the basis for how machine learning works, and the data used is called a training data set. The computer also needs analytical software and algorithms, as well as a deployment environment \\u2014 usually a server that is adapted to meet the user\\u2019s computing needs. There are different types of learning, which can vary depending on whether or not you know the response you are looking for, the type of data being analyzed, the data environment under consideration, and the type of analytical action being taken (statistics, comparisons, image recognition, etc.). The learning algorithms differ depending on the task at hand, and the computing power they require will also be affected.\\nMachine learning usually involves two steps. The first is the development of the model from the set of test data, also known as observation data. This step involves defining the task that the user wants to process (detecting the presence of an element in a photo, detecting a statistical recurrence, responding to a sensor\\u2019s signal, etc.). This is the testing or \\\"training\\\" phase. The second stage involves putting the model into production. It can be optimised with new data. Some systems may continue learning during the production phase \\u2014 but the user needs to ensure that they get feedback on the results produced, so that they can optimise the model and manage the machine. Others can continue their learning alone, and develop independently.\\nThe quality of the learning is dependent on several factors:\\nThe number of relevant examples that the computer can consider. The more data, the more accurate the results\\nThe number of characteristics describing the examples. The simpler and more precise they are (size, weight, quantity, speed, etc.), the quicker and more accurate the analysis will be.\\nThe quality of the database used. If too much data is missing, this will affect the process. False or exaggerated data can also distort results.\\nThe prediction algorithm will be more accurate, and the analysis will be more relevant if these elements are taken into account. Once the machine learning project is defined and the databases are ready, you can start the machine learning process.\\nMake your machine learning project a success with OVHcloud:\\nWe have always been committed to bringing technology to all business sectors. We believe that with the potential AI represents, it should not be reserved solely for IT giants or major companies. We want to help you and support you as much as possible in launching ambitious AI and machine learning projects. Artificial intelligence boosts efficiency for businesses, and facilitates decision-making. OVHcloud offers tools to help you meet business challenges, such as predictive analysis of datasets, and make it easy to use for all user profiles. We support our customers in developing their artificial intelligence system.\\nWith OVHcloud, you can collect and prepare your data using our Data Analytics solutions. You can model your machine learning project step by step, and deploy your model in just a few clicks. You can choose from a range of tools and frameworks, such as TensorFlow, PMML or ONNX.\\nOVHcloud solutions offer a number of advantages when it comes to developing your machine learning project:\\nConfidentiality for your data\\nWe are committed to keeping your personal data confidential. Data sovereignty is a vital aspect of our company philosophy, so you can recover your data whenever you need to.\\nComputing power\\nBy automating deployments and our infrastructures, we can offer you unrivalled computing power at competitive prices.\\nOPEN SOURCE\\nIn the world of data, open-source solutions are now the most mature and high-performance products on the market. OVHcloud values the importance of basing its solutions on open-source software, like the Apache Hadoop and Apache Spark suites.\\nExplore our range of Public Cloud products\\nAI & machine learning\\nArtificial intelligence (AI) is often seen as an aspect of data science reserved only for those who are experienced in the field. At OVHcloud, we believe in the outstanding potential of this practice in all business sectors. And we believe that its complexity should not stand in the way of the use of big data and machine learning.\\nExplore our AI and ML solutions\\nGPU\\nGPU instances integrate NVIDIA graphic processors to meet the requirements of massively parallel processing. Since they are integrated into the OVHcloud solution, you get the advantages of on-demand resources and hourly billing.\\nExplore our Cloud GPU instances\\nAI Training\\nLaunch your AI training tasks in the cloud, without having to worry about how the infrastructure works. AI Training enables data scientists to focus on their core business, without having to worry about orchestrating computing resources.\\nExplore our AI Training solution\\nBack to top\\nTools\\nMy customer account\\nWebmail\\nAPI\\nProcedure\\nMailing lists\\nStatus\\nWhois\\nDomain contact\\nReport abuse (abuse@ovh.net)\\nWHOIS information disclosure request\\nIntellectual property\\nTrademarks\\nSupport\\nHelp centre\\nGuides\\nLearning Centre\\nGlossary\\nCommunity\\nSupport levels\\nContact us\\nOVHcloud Customer Service\\nMonday to Friday, 9am to 6pm\\n+44333 370 0425\\nLocal call rate\\nNews\\nPress\\nBlog\\nSocial networks\\nKeep in touch\\n\\u00a9 Copyright 1999-2026 OVH SAS.\\nTerms & conditions\\nContracts\\nData protection\\nManage my cookies\\nRights and obligations of domain name holders\\nICANN documentation for domain name holders\\nPayments\\nSitemap\\nAbout OVHcloud\\nOVHcloud Careers\\nIn accordance with the 2006\\/112\\/CE Directive, modified on 01\\/01\\/2015, prices incl. VAT may vary according to the customer's country of residence\\n(by default, the prices displayed are inclusive of the UK VAT in force).\\n\"}"
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"system": "The content you will receive is in json format. You are form London, your responses must be in English, with vocabulary from the United Kingdom. The response should be in json format. Do not return a linguistic consistency error Content format in json: {\"data\":\"content\"} Response format in case of success: { \"status\" : \"ok\", \"response\": [\"data 1\", \"data 2\", \"data etc.\"] } Response format in case of error: {\"status\":\"error\",\"message\":\"error message\"} Summarize in English as a bullet-point list with simple words all the information and numerical data provided to you."
},
"content": "{\"data\":\"Open menu\\nWebmail\\nNotifications\\nBack to menu\\nNo pending order\\nNo pending ticket\\nMy customer account\\nBack to menu\\nMy customer account\\nMy bills\\nMy products and services\\nMy payment methods\\nMy orders\\nMy contacts\\nMy support tickets\\nContact Sales\\nSupport\\nBack to menu\\nHelp center\\nSupport Levels\\nProfessional Services\\nCommunities\\nBack to menu\\nOVHcloud Community\\nOVHcloud Blog\\nEvents\\nLearn\\nBack to menu\\nDocumentation\\nTutorials\\nUse cases\\nDeveloper Centre\\nCompliance\\nCase Studies\\nVideos\\nStories\\nTraining\\nUnited Kingdom [\\u00a3]\\nBack to menu\\nEurope\\nDeutschland [\\u20ac]\\nEspa\\u00f1a [\\u20ac]\\nFrance [\\u20ac]\\nIreland [\\u20ac]\\nItalia [\\u20ac]\\nNederland [\\u20ac]\\nPolska [PLN]\\nPortugal [\\u20ac]\\nUnited Kingdom [\\u00a3]\\nAmerica\\nCanada (en) [$]\\nCanada (fr) [$]\\nUnited States [$]\\nAm\\u00e9rica Latina [$]\\nAfrica\\nMaroc [Dhs]\\nS\\u00e9n\\u00e9gal [FCFA]\\nTunisie [DT]\\nOceania\\nAustralia [A$]\\nAsia\\nSingapore [S$]\\nAsia [US$]\\nIndia [\\u20b9]\\nWorld\\nWorld [$]\\nWorld [\\u20ac]\\nOpen login menu\\nWelcome to OVHcloud.\\nLog in to order, manage your products and services, and track your orders.\\nMy customer account\\nWebmail\\nBare Metal & VPS\\nBack to menu\\nBare Metal & VPS\\nDedicated Servers\\nBack to menu\\nDedicated Servers\\nProducts\\nDiscover our dedicated servers\\nRise Servers\\nNew\\nThe most affordable bare-metal servers offered by OVHcloud.\\nAdvance Servers\\nNew\\nVersatile servers for small and medium businesses.\\nGame Servers\\nNew\\nFor video games and streaming platforms.\\nStorage Servers\\nServers for archiving, backup, and distributed storage.\\nScale Servers\\nNew\\nSpecifically designed for complex, high-resilience infrastructures.\\nHigh Grade Servers\\nThe most powerful servers, optimised for critical loads.\\nOperating systems and applications\\nOperating systems and applications adapted to your needs\\nDedicated server availability by region\\nThe availability of our servers in various regions around the world\\nBare Metal Wholesale\\nGet a full rack of Advance, Scale or High Grade servers.\\nUse Cases\\nAZ and resilience\\nGrid computing\\nSAP HANA\\nVirtualisation and containerisation\\nWebsite Business Application\\nHyperconverged Infrastructure\\nSoftware-defined Storage\\nBig Data and Analytics\\nArchiving and Backup\\nAI, Machine Learning & Deep Learning\\nConfidential Computing\\nDatabase\\nGaming\\nHigh Performance Computing\\nEco Dedicated Servers\\nBack to menu\\nEco Dedicated Servers\\nEco Dedicated Servers\\nExplore our Dedicated Servers\\nKimsufi Servers\\nLow-cost servers to get you started\\nSo you Start Servers\\nA range of dedicated servers that are perfect for startups and very small businesses\\nRise Servers\\nNew\\nTried-and-tested Intel and AMD platforms for competitive performance and price\\nOperating systems and distributions\\nFind the versions compatible with your Eco server\\nUse cases\\nWebsite and business application\\nEmail server\\nNew\\nData storage\\nNew\\nVirtual private servers \\u2013 VPS\\nBack to menu\\nVirtual private servers \\u2013 VPS\\nVirtual private servers \\u2013 VPS\\nVPS\\nNew\\nOur new VPS with maximum resources, the best prices, instant scalability, and reinforced security, available in all our regions.\\nDistributions and licences\\nSee all our distributions and licences available on VPS\\nOptions\\nCustomise your VPS with our advanced options\\nHelp\\nUse cases\\nAutomated workflows with n8n\\nNew\\nWordPress multisite platform\\nVideo game server\\nTest server with VPS\\nHosting trading applications on a Forex VPS\\nManaged Bare Metal\\nBack to menu\\nManaged Bare Metal\\nManaged Bare Metal\\nManaged Bare Metal Essentials powered by VMware\\u00ae\\nYour virtual infrastructure managed by OVHcloud\\nStorage and backups\\nBack to menu\\nStorage and backups\\nStorage and backup\\nExplore all our solutions\\nEnterprise File Storage\\nFully managed file storage based on NetApp ONTAP Select\\nHA-NAS\\nCentralised storage or backup space for your file data\\nCloud Disk Array\\nA scalable storage solution based on CEPH technology\\nVeeam Enterprise Plus\\nThe ultimate solution for protecting your data in a way that fits your needs\\nHYCU for OVHcloud\\nSimplify the backup and migration of your Nutanix workloads\\nUse cases\\nData storage for your Linux servers\\nData storage for your virtual machines\\nNetwork\\nBack to menu\\nNetwork\\nNetwork\\nAdditional IP\\nAssign and move dynamic IP addresses from one service to another\\nOVHcloud Load Balancer\\nBalance your application load across multiple backend servers\\nPrivate Network (vRack)\\nConnect all your OVHcloud services together with an isolated private network\\nOVHcloud Link Aggregation\\nGet a redundant, high-bandwith private network\\nOVHcloud Connect\\nConnect your datacentre to OVHcloud\\nPublic Bandwith\\nUpgrade your default guaranteed bandwidth\\nCDN Infrastructure\\nA dedicated CDN to complement your OVHcloud products\\nBring Your Own IP (BYOIP)\\nBring your IP addresses and simplify your migration to OVHcloud\\nNetwork Security\\nBack to menu\\nNetwork Security\\nNetwork Security\\nAnti DDoS infrastructure\\nKeep your infrastructures protected against DDoS attacks\\nGame DDoS Protection\\nProtect your gaming and e-sport business with best-in-class security solution\\nDNSSEC\\nProtect your data from DNS cache poisoning\\nSSL Gateway\\nSimplest way to enable security on your webpage. Without any effort!\\nIdentity, Security & Operations\\nBack to menu\\nIdentity, Security & Operations\\nIdentity, Security & Operations\\nIdentity and Access Management (IAM)\\nSecure your access management and enhance your productivity\\nLogs Data Platform\\nComplete platform to collect, store and view your logs\\nKey Management Service (KMS)\\nSecure your data across all your OVHcloud services from one central place\\nSecret Manager\\nProfessionally manage of all your secrets in one place\\nService Logs\\nMonitor your cloud environment\\u2019s performance and security\\nBare Metal Pod\\nBack to menu\\nBare Metal Pod\\nBare Metal Pod\\nSecNumCloud-qualified Bare Metal Pod\\nBare Metal performance in a SecNumCloud compliant sovereign environment.\\nUse Cases\\nData sovereignty\\nSecNumCloud-qualified solutions\\nDomain Hosting Email\\nBack to menu\\nDomain Hosting Email\\nDomain name\\nBack to menu\\nDomain name\\nDomain name\\nSearch for your domain name\\nTransfer your domain name to OVHcloud\\nRenew your domain name\\nSearch the secondary market (aftermarket)\\nQuick access\\nDomain name prices\\nWHOIS - Search for domain information\\nDNS server\\nSpecial offers\\nNew\\nWeb Hosting\\nBack to menu\\nWeb Hosting\\nWeb Hosting\\nAll Web packs\\nAdditional databases\\nSSL option\\nCDN option\\nQuick access\\nHow do I create a website?\\nHost your WordPress website\\nCreate your website in 1 click\\nCreate your online store\\nEmail & collaborative solutions\\nBack to menu\\nEmail & collaborative solutions\\nEmail & collaborative solutions\\nAll Email plans\\nAll Exchange plans\\nOffice 365 licences\\nSMS\\nBack to menu\\nSMS\\nSMS\\nSend your Pro text messages\\nPublic Cloud\\nBack to menu\\nPublic Cloud\\nPublic Cloud\\nBack to menu\\nPublic Cloud\\nPublic Cloud\\nDiscover Public Cloud\\nService ecosystem\\nAutomate your setup, propel your business\\nPricing\\nView our simple, accessible pricing\\nPublic Cloud free trial\\nTry out our solutions at no cost\\nSavings Plans\\nNew\\nOpt for a 1 to 36 month subscription and save on your Public Cloud resources.\\nThe advantages of the Public Cloud solution\\nDiscover cloud computing solutions to meet your needs\\nCloud Computing\\nFind out more about this growing practice\\nCertification & compliance\\nExplore how OVHcloud builds trusted solutions\\nTrusted Zones\\nDeploy your critical data in high-security zones\\nAvailability by region\\nExplore our catalogue coverage by region\\nLocal Zones\\nNew\\nDeploy cloud services closer to your users\\nOur documentation\\nRead our guides and tutorials\\nCompute\\nBack to menu\\nCompute\\nCompute\\nExplore all our Compute products\\nVirtual Machine Instances\\nGet versatile instances that can be adapted to suit all your needs.\\nCloud GPU\\nSpeed up your workloads with high-performance GPU instances.\\nMetal Instances\\nCombine the power of Bare Metal with cloud automation\\nOur documentation\\nBrowse our documentation for the Compute range\\nEn route to PaaS\\nFocus on your applications & become more competitive\\nLocal Zone\\nNew\\nDeploy cloud services closer to your users\\nStorage\\nBack to menu\\nStorage\\nStorage\\nExplore all our Storage products\\nBlock Storage\\nCreate storage volumes, which can be used as additional disks\\nObject Storage\\nGet S3-compatible unlimited storage on demand\\nCold Archive\\nHighly economical archiving for infrequently accessed data.\\nLocal Zone\\nNew\\nDeploy cloud services closer to your users\\nOur documentation\\nBrowse our documentation for the Storage range\\nNetwork\\nBack to menu\\nNetwork\\nNetwork\\nExplore all our Network products\\nPrivate Network\\nDeploy private networks via the OVHcloud vRack\\nLoad Balancer\\nManage variable traffic by spreading it across multiple resources\\nFloating IP\\nAssign and move your public IP from one service to another\\nGateway\\nManage a single connection point between your private network & internet\\nOur documentation\\nView our documentation for the Network range\\nContainers & Orchestration\\nBack to menu\\nContainers & Orchestration\\nContainers & Orchestration\\nExplore all our Containers & Orchestration products\\nManaged Kubernetes Service\\nOrchestrate your containerised applications with a CNCF-certified Kubernetes cluster\\nLoad Balancer for Managed Kubernetes Service\\nManage variations in activity by distributing traffic across multiple resources\\nManaged Rancher Service\\nNew\\nSimplified and centralised management of your Kubernetes clusters\\nManaged Private Registry\\nManage your images of containers and helmchart on a secure private registry\\nOur documentation\\nView our documentation for the Containers & Orchestration range\\nEn route to PaaS\\nFocus on your applications & become more competitive\\nDatabases\\nBack to menu\\nDatabases\\nDatabases\\nExplore all our Databases products\\nMongoDB\\nDocument-oriented NoSQL engine. Try for free with Free Tier\\nMySQL\\nPopular relational database that adapts to your usage\\nPostgreSQL\\nThe top open-source relational database engine\\nValkey\\nSmart in-memory storage\\nOur Documentation\\nView our documentation for the Databases range\\nEn route to PaaS\\nFocus on your applications & become more competitive\\nAnalytics\\nBack to menu\\nAnalytics\\nAnalytics\\nExplore all our Analytics products\\nKafka\\nQueueing solution for deploying your event-driven architectures\\nKafka Connect\\nExtension simplifying the ingestion of your sources to Apache Kafka\\nKafka MirrorMaker\\nReplication ensuring high availability for your Kafka clusters\\nLogs Data Platform\\nComplete platform to collect, store and view your logs\\nOpenSearch\\nA dedicated engine for indexing, searching and analyzing data\\nClickHouse\\nNew\\nUltra-fast data analysis at your fingertips\\nManaged Dashboards\\nGrafana platform for creating dashboards\\nOur documentation\\nView our documentation for the Analytics range\\nEn route to PaaS\\nFocus on your applications & become more competitive\\nData Platform\\nNew\\nBack to menu\\nData Platform\\nData Platform\\nBrowse all our Data Platform products\\nDiscover the OVHcloud Data Platform\\nNew\\nCarry out your Data & Analytics projects in record time and with ease\\nData Catalog\\nNew\\nMore than 50 connectors for all your data sources\\nLakehouse Manager\\nNew\\nUnified data warehouse and data lake storage, based on Apache Iceberg\\nData Processing Engine\\nNew\\nAutomate the execution and orchestration of your ETL\\/ELT workloads\\nAnalytics Manager\\nNew\\nCreate your dashboards and launch your requests with the Trino engine\\nApplication Services\\nNew\\nSDK and serverless services to deploy your APIs and Data applications\\nControl Center\\nNew\\nMonitor metrics, manage logs and alerts for your environments\\nAI & Machine Learning\\nBack to menu\\nAI & Machine Learning\\nAI & Machine Learning\\nExplore all our AI & Machine learning products\\nAI & Quantum Notebooks\\nLaunch your Jupyter or VS Code notebooks in the cloud, and choose from our native AI or quantum frameworks\\nAI Training\\nTrain your AI models\\nAI Deploy\\nDeploy machine learning models & get your predictions\\nAI Endpoints\\nNew\\nIntegrate generative AI models into your applications easily and securely via standard APIs.\\nOur documentation\\nBrowse our documentation for the AI & Machine Learning range\\nEn route to PaaS\\nFocus on your applications & become more competitive\\nQuantum Computing\\nBack to menu\\nQuantum Computing\\nQuantum Computing\\nExplore all our Quantum Computing products\\nQuantum Emulators\\nNew\\nSimulate your quantum algorithms on ready-to-use notebooks\\nQuantum Processing Units (QPU)\\nNew\\nAccess real quantum computers through our Quantum Platform\\nWhat is Quantum computing?\\nLearn more about the next accelerated computing revolution and how to develop today\\u2019s solutions with tomorrow\\u2019s quantum computers\\nIdentity, Security & Operations\\nBack to menu\\nIdentity, Security & Operations\\nIdentity, Security & Operations\\nDiscover all our Identity, Security & Operations products\\nIdentity and Access Management (IAM)\\nSecure your access management and improve your productivity\\nLogs Data Platform\\nComplete platform to collect, store and view your logs\\nKey Management Service (KMS)\\nSecure your data across all your OVHcloud services from one place\\nSecret Manager\\nProfessionally manage of all your secrets in one place\\nServices Logs\\nMonitor the performance and security of your cloud environment\\nHosted Private Cloud\\nBack to menu\\nHosted Private Cloud\\nVMware\\nBack to menu\\nVMware\\nVMware on OVHcloud\\nDiscover VMware on OVHcloud\\nPublic VCF as a Service\\nNew\\nShared and managed VMware solution, powered by VMware Cloud Foundation\\nManaged VMware vSphere solutions\\nManaged VMware solution for all enterprises\\nSecNumCloud-qualified Managed VMware vSphere\\nANSSI-qualified VMware Trusted Zone solution\\nSolutions\\nCompare VMware solutions\\nSAP on OVHcloud\\nDatacentre extension and migration\\nHybrid and Multi-Cloud Solutions\\nDisaster recovery solutions\\nEuropean Trusted Zone Solutions\\nView all solutions\\nNutanix\\nBack to menu\\nNutanix\\nHosted Private Cloud\\nNC2 on OVHcloud\\nNew\\nNutanix Cloud Clusters (NC2) on OVHcloud\\nNutanix on OVHcloud\\nOur scalable, ready-to-use Nutanix hyperconverged platform (HCI)\\nSecNumCloud-qualified Bare Metal Pod\\nNew\\nNutanix-certified servers available in SecNumCloud-qualified Bare Metal Pod\\nHYCU for OVHcloud\\nSimplify the backup and migration of your Nutanix workloads\\nVeeam Enterprise for all your backups\\nA dedicated Veeam Backup Replication solution for all your backups\\nUse cases\\nMigrate and manage your data\\nDisaster Recovery Plan (DRP)\\nHyperconvergence, savings and ecology\\nDisaster Recovery (DRaaS)\\nSAP HANA\\nBack to menu\\nSAP HANA\\nSAP HANA\\nSAP HANA on Private Cloud\\nThe solution that facilitates your SAP deployments in a sovereign cloud\\nSolutions\\nSAP on OVHcloud\\nOn-Prem Cloud Platform\\nBack to menu\\nOn-Prem Cloud Platform\\nOn-Prem Cloud Platform\\nOn-Prem Cloud Platform (OPCP)\\nOPCP enables you to deploy your services on-premises or at the edge, with complete autonomy and security. Air-gapped by design.\\nInfra as a Service\\nCore infrastructure to build and operate your on-premises cloud\\nPlatform as a Service\\nCloud-native environments and ready-to-use services\\nCloud ready\\nLanding Zone to deploy and manage workloads easily\\nStorage & Backup\\nBack to menu\\nStorage & Backup\\nStorage and backup\\nDiscover all the storage solutions\\nVeeam option for VMware backup\\nThe Veeam Managed Backup solution for backing up your virtual machines\\nZerto option for your VMware Disaster Recovery Plan\\nMulti-site Disaster Recovery Plan solution for your VMware clusters\\nVeeam option for Public VCF as a Service\\nA dedicated Veeam Backup Replication solution for all your backups\\nVeeam Enterprise - Licences\\nA dedicated Veeam Backup Replication solution for all your backups\\nHYCU for OVHcloud\\nSimplify the backup and migration of your Nutanix workloads\\nObject Storage\\nGet S3-compatible unlimited storage on demand\\nCold Archive\\nGet long-term archiving at the best price\\nNetApp - Enterprise File Storage\\nFully managed file storage based on NetApp ONTAP Select\\nUse cases\\nBackup and Disaster Recovery\\nBusiness continuity\\nDisaster Recovery for Managed VMware vSphere\\nDisaster Recovery for Nutanix on OVHcloud\\nNetwork\\nBack to menu\\nNetwork\\nNetwork\\nAdditional IP\\nAssign and move dynamic IP addresses from one service to another\\nOVHcloud Load Balancer\\nBalance your application load across multiple backend servers\\nPrivate Network (vRack)\\nConnect all your OVHcloud services together with an isolated private network\\nOVHcloud Connect\\nConnect your datacentre to OVHcloud\\nCDN Infrastructure\\nA dedicated CDN to complement your OVHcloud products\\nBring Your Own IP (BYOIP)\\nBring your IP addresses and simplify your migration to OVHcloud\\nNetwork Security\\nBack to menu\\nNetwork Security\\nNetwork Security\\nAnti-DDoS infrastructure\\nKeep your infrastructures protected against DDoS attacks\\nDNSSEC\\nProtect your data from DNS cache poisoning\\nSSL Gateway\\nSimplest way to enable security on your webpage. Without any effort!\\nIdentity, Security & Operations\\nBack to menu\\nIdentity, Security & Operations\\nIdentity, Security & Operations\\nIdentity and Access Management (IAM)\\nSecure your access management and enhance your productivity\\nLogs Data Platform\\nComplete platform to collect, store and view your logs\\nKey Management Service (KMS)\\nSecure your data across all your OVHcloud services from one central place\\nSecret Manager\\nProfessionally manage of all your secrets in one place\\nService Logs\\nMonitor your cloud environment\\u2019s performance and security\\nCompliance and certifications\\nBack to menu\\nCompliance and certifications\\nCompliance and certifications\\nFull list of standards and regulations\\nGDPR\\nCompliance with the General Data Protection Regulation (EU) 2016\\/679\\nSecNumCloud\\nANSSI Security Visa Qualification\\nHDS and healthcare data hosting\\nHosting healthcare data in the UK and across Europe\\nHIPAA and HITECH\\nHosting healthcare data in the US\\nPCI DSS\\nFinancial data hosting\\nISO\\/IEC 27001, 27017 and 27018\\nInformation Security Management\\nISO\\/IEC 27701\\nManaging the security of personal data processing\\nISO 14001\\nManaging environmental impacts\\nISO 50001\\nEnergy management\\nSOC 1, 2 and 3\\nAICPA SSAE 16\\/ISAE 3402 type II attestation and reports\\nEBA and ACPR\\nCompliance for financial services operators in Europe\\nG-Cloud\\nPublic sector cloud service delivery in the UK\\nSolutions\\nBack to menu\\nSolutions\\nUse Cases\\nBack to menu\\nUse Cases\\nUse Cases\\nMigrate to the cloud\\nHybrid cloud & Multi Cloud\\nApp Modernization\\nCloud Native App\\nArtificial Intelligence\\nBig Data Analytics\\nData Management\\nHigh-Performance Workloads\\nLarge Data set storage\\nGrid Computing\\nMove to PaaS\\nBackup and disaster recovery\\nBusiness Continuity\\nTrusted Zone\\nSecNumCloud Environment\\nNetwork Protection\\nCloud Security\\nExtension & Migration Datacenter\\nDatacenter transformation\\nElevate your brand reputation\\nPrioritise financial stability\\nProtect your business against cyberthreats\\nIndustry\\nBack to menu\\nIndustry\\nIndustry\\nPublic Sector\\nA trusted solution for governments and authorities\\nHealthcare\\nThe trusted solution for the healthcare sector\\nFinancial Service\\nOur solutions for financial services operators\\nManufacturing\\nThe trusted cloud solution for European manufacturers\\nSoftware\\/Information Technology\\nSaaS and PaaS solutions from OVHcloud Partner Software Publishers\\nGaming\\nCloud solutions for companies and individuals in the gaming industry\\nBlockchain\\nFree up your blockchain projects with OVHcloud solutions\\nOrganisation Type\\nBack to menu\\nOrganisation Type\\nOrganisation Type\\nEnterprise\\nSolutions for enterprise-level digital transformation\\nSoftware publishers (SaaS\\/PaaS)\\nSaaS and PaaS solutions from OVHcloud Partner Software Publishers\\nSystem Integrator\\nSolutions for integrators, service providers and consulting firms\\nGovernment \\/ Administrations\\nTrusted solutions for governments and authorities\\nStartup\\nSupport solutions for start-ups\\nScaleup\\nSupport solutions for scale-ups\\nTechnology\\nBack to menu\\nTechnology\\nTechnology\\nVeeam\\nProtect your data with Veeam solutions offered by OVHcloud\\nVMware by Broadcom\\nVMware by Broadcom and OVHcloud solutions for all your projects\\nNutanix\\nAccelerate and simplify your hybrid multicloud journey with Nutanix on OVHcloud solution\\nHYCU\\nThe backup solution trusted by Nutanix users\\nSAP\\nOur SAP on OVHcloud solutions for SAP environments hosting in a sovereign cloud\\nNetApp\\nNetApp storage solutions with cost control and high performance\\nNvidia\\nNvidia GPU solutions for accelerating your innovation and AI projects\\nMongoDB\\nMongoDB solutions that simplify data management\\nOpenStack\\nOpenStack solutions integrated with OVHcloud for cloud infrastructure\\nIntel\\nExpert solutions powered by Intel\\u00ae Xeon\\u00ae that accelerate cloud processes\\nAMD\\nTop-of-the-line cloud solutions powered by AMD processors\\nHadoop Cloudera\\nYour 100% managed Cloudera solution with Claranet\\nEcosystem\\nBack to menu\\nEcosystem\\nEcosystem\\nDiscover the OVHcloud partner ecosystem\\nPartner Program\\nAn initiative dedicated to our reseller partners, integrators, administrators and consultants.\\nOpen Trusted Cloud\\nAn ecosystem of labelled SaaS and PaaS solutions, hosted in our open, reversible and reliable cloud\\nStartup Program\\nA support program for startups and scaleups to accelerate their growth\\nOVHcloud Labs\\nThe innovation zone to test our cutting-edge technologies before they are officially launched on the market\\nOur Ecosystem events\\nFind all the events dedicated to our partner ecosystem: webinars, conferences, and more\\nOVHcloud Ecosystem Awards\\nDiscover our OVHcloud Ecosystem awards that rewards our ecosystem leaders of the Year per category\\nTraining & Certification\\nDevelop your expertise with the training and certifications available to OVHcloud Partner Program members.\\nQuick Access\\nFind a partner\\nSign up to the OVHcloud Partner Program\\nSign up to the OVHcloud Startup Program\\nPrice comparison\\nPartner Portal\\nFAQ Partner Program\\nAbout\\nBack to menu\\nAbout\\nAbout\\nWho we are\\nNewsroom\\nGlobal infrastructure\\nOur datacenters\\nOur Local Zones\\nBackbone network\\nJoin the OVHcloud adventure\\nPatent Pledge\\nLegal\\nGDPR - Personal data protection\\nOur commitments\\nInnovation\\nSustainable cloud\\nTrusted cloud\\nInvestor Relations\\nEnvironmental Impact Tracker\\nSummit\\nOpen search bar\\nClose search bar\\nNo result\\nProducts\\nSolutions\\nPartners\\nDocumentation\\nArticles\\nSee all results\\nWhat is machine learning?\\nLearning Centre\\nWhat is machine learning?\\nWe generate more and more information every day, thanks to the multiplicity of technologies we use (smartphones, computers, tablets, connected devices, etc.). All of these devices generate a massive amount of data. An average person generates 1.7 MB of data per second in 2020. Big data is a huge source of information, stored in digital databases. But without adequate processing and an effective strategy, this mass would be a collection of problematic bytes to pile up. This is where machine learning comes in and makes the most of it.\\nWhat is machine learning?\\nThe first machine learning algorithms were developed in 1950. Machine learning, or machine learning, is both a technology and a science (Data Science) that allows a computer to perform a learning process without having been programmed for that purpose. This technique \\u2014 which is linked to artificial intelligence (AI) \\u2013 is designed to highlight patterns of statistical repetition, and derive statistical predictions from them. Data\\nmining\\n, which is the extraction of information from a large amount of data, serves as the raw material for machine learning to highlight patterns for statistical prediction. This is why big data (all of the data generated and stored) is an integral part of machine learning. The larger the set that reveals trends, the more accurate the predictions.\\nMore specifically, the learning algorithm applied enables the computer to refine its analysis and responses, based on empirical data from the associated database. Machine learning is a great learning model for businesses, because it allows them to harness the power of the data generated by their customers or activity. Artificial intelligence is thus a major challenge if they are to succeed.\\nThere are several types of learning that are classified according to existing data during the learning phase. If the response to the defined task is already known, the data is referred to as \\u2018labelled\\u2019. This is what is known as supervised learning. Depending on whether the data is discrete or continuous, classification or regression is used. If the learning takes place step by step, with a reward system in place for each task performed correctly, then it is known as reinforcement learning. The most common type of learning is unsupervised learning, which involves searching without labels. It aims to predict a result, without using known answers beforehand.\\nTwo approaches to machine learning\\nSupervised machine learning\\nSupervised machine learning is a type of machine learning where a model is trained on a labeled data set. This means that each example in the data set has an input (or characteristic) and a corresponding output (or label). The goal is to learn a function that, from input characteristics, correctly predicts output labels for new data.\\nThe basic process of supervised machine learning is as follows.\\nData Collection: Gather a data set with labeled samples.\\nData division: separate data into training and test sets.\\nTraining: Use the training assembly to learn a model that connects input characteristics to output labels.\\nValidation and testing: Evaluate the model performance on the test set to verify its accuracy and generalizability.\\nSupervised machine learning is used in several types of activity: for classification (e.g., determining a category like spam) or predicting a numerical value (e.g., estimating the price of a house based on its characteristics).\\nSupervised learning is used in many practical applications, such as speech recognition, fraud detection and referral systems.\\nMachine learning not supervised\\nUnsupervised machine learning is a type of machine learning where a model is trained on unlabeled data. Unlike supervised learning, there is no predefined exit. The goal is to find hidden structures or patterns in the data.\\nMain types of unsupervised learning:\\nClustering: dividing data into similarity-based groups or clusters (e.g. grouping customers with similar buying behaviors);\\nDimensionality reduction: Simplify data by reducing the number of characteristics while retaining most of the information (for example, the principal components method or PCA).\\nCommon examples of unsupervised machine learning use:\\nCustomer segmentation: identify groups of customers with similar behaviors or characteristics;\\nanomaly detection: identify unusual data that does not follow general behavior (e.g. detect fraudulent transactions).\\nUnsupervised learning is useful for exploring data and discovering patterns or relationships without the need for prior knowledge of expected labels or outcomes.\\nWhat is machine learning used for?\\nThe power and advantage of machine learning lies in its ability to process a huge volume of data that is impossible for the human brain to process. Industries that gather a high volume of data need a solution for processing it, and extracting information that can be used for decision-making. Predictive analysis of this data enables the computer to anticipate specific situations. This is what machine learning is all about. Let us consider the financial services sector, for example. Machine learning is used to detect fraud, illegal conduct and other elements that are key for financial institutions to work properly.\\nThe growing volume of transactional data we generate is also used by companies to target their customers based on their purchasing behaviour, by identifying repetitions. The websites and pages we visit also generate data that can be used by machine learning to set our preferences. It is therefore clear that this data processing technique, without the need for human intervention, is a major asset for companies wishing to take advantage of the mass of information available to them. It is unlikely that a human being would be able to make use of this data themselves, because the volume of data to process is just so high. Take the large companies owned by GAFAM, for example: the implementation of AI and machine learning in their processes has become a necessity, due to the large usable data stream that they generate.\\nWith data being generated in ever-increasing volumes, a growing number of companies will also need to integrate this technology into their structure in order to make use of the information available to them. Connected devices, for example, are becoming increasingly present in our daily lives. By 2019, more than 8 billion connected objects had entered our society, allowing us to collect more data on our rhythm of life, our consumption, our habits, based on our voice recognition. All of this represents a huge mass of critical data for companies, and machine learning helps us identify the elements that are relevant and useful. Without a doubt, there is a lot at stake here. Big data plays a vital part in the development of many technologies for modern society \\u2014 like facial recognition, self-driving cars, robotics, and smart home technology, for example. But to create this technology, companies must learn how to implement this asset in a suitable way. This technology isn\\u2019t just for AI-savvy development teams. Many companies are embarking on the adventure of machine learning by choosing turn-key solutions that are adapted to fit their objectives.\\nHow machine learning works\\nMachine learning works based on \\u201cexperience\\u201d. The computer retrieves a high volume of data, and uses it to analyse and predict situations. The goal of the process is for the machine to independently create an \\u201cinternal plan\\u201d, which it can use to identify the key elements that the user wants to target. It will need to experiment with different examples and tests in order to progress. This is why we talk about learning.\\nTo train itself and learn, the computer needs learning data. Data mining is the basis for how machine learning works, and the data used is called a training data set. The computer also needs analytical software and algorithms, as well as a deployment environment \\u2014 usually a server that is adapted to meet the user\\u2019s computing needs. There are different types of learning, which can vary depending on whether or not you know the response you are looking for, the type of data being analyzed, the data environment under consideration, and the type of analytical action being taken (statistics, comparisons, image recognition, etc.). The learning algorithms differ depending on the task at hand, and the computing power they require will also be affected.\\nMachine learning usually involves two steps. The first is the development of the model from the set of test data, also known as observation data. This step involves defining the task that the user wants to process (detecting the presence of an element in a photo, detecting a statistical recurrence, responding to a sensor\\u2019s signal, etc.). This is the testing or \\\"training\\\" phase. The second stage involves putting the model into production. It can be optimised with new data. Some systems may continue learning during the production phase \\u2014 but the user needs to ensure that they get feedback on the results produced, so that they can optimise the model and manage the machine. Others can continue their learning alone, and develop independently.\\nThe quality of the learning is dependent on several factors:\\nThe number of relevant examples that the computer can consider. The more data, the more accurate the results\\nThe number of characteristics describing the examples. The simpler and more precise they are (size, weight, quantity, speed, etc.), the quicker and more accurate the analysis will be.\\nThe quality of the database used. If too much data is missing, this will affect the process. False or exaggerated data can also distort results.\\nThe prediction algorithm will be more accurate, and the analysis will be more relevant if these elements are taken into account. Once the machine learning project is defined and the databases are ready, you can start the machine learning process.\\nMake your machine learning project a success with OVHcloud:\\nWe have always been committed to bringing technology to all business sectors. We believe that with the potential AI represents, it should not be reserved solely for IT giants or major companies. We want to help you and support you as much as possible in launching ambitious AI and machine learning projects. Artificial intelligence boosts efficiency for businesses, and facilitates decision-making. OVHcloud offers tools to help you meet business challenges, such as predictive analysis of datasets, and make it easy to use for all user profiles. We support our customers in developing their artificial intelligence system.\\nWith OVHcloud, you can collect and prepare your data using our Data Analytics solutions. You can model your machine learning project step by step, and deploy your model in just a few clicks. You can choose from a range of tools and frameworks, such as TensorFlow, PMML or ONNX.\\nOVHcloud solutions offer a number of advantages when it comes to developing your machine learning project:\\nConfidentiality for your data\\nWe are committed to keeping your personal data confidential. Data sovereignty is a vital aspect of our company philosophy, so you can recover your data whenever you need to.\\nComputing power\\nBy automating deployments and our infrastructures, we can offer you unrivalled computing power at competitive prices.\\nOPEN SOURCE\\nIn the world of data, open-source solutions are now the most mature and high-performance products on the market. OVHcloud values the importance of basing its solutions on open-source software, like the Apache Hadoop and Apache Spark suites.\\nExplore our range of Public Cloud products\\nAI & machine learning\\nArtificial intelligence (AI) is often seen as an aspect of data science reserved only for those who are experienced in the field. At OVHcloud, we believe in the outstanding potential of this practice in all business sectors. And we believe that its complexity should not stand in the way of the use of big data and machine learning.\\nExplore our AI and ML solutions\\nGPU\\nGPU instances integrate NVIDIA graphic processors to meet the requirements of massively parallel processing. Since they are integrated into the OVHcloud solution, you get the advantages of on-demand resources and hourly billing.\\nExplore our Cloud GPU instances\\nAI Training\\nLaunch your AI training tasks in the cloud, without having to worry about how the infrastructure works. AI Training enables data scientists to focus on their core business, without having to worry about orchestrating computing resources.\\nExplore our AI Training solution\\nBack to top\\nTools\\nMy customer account\\nWebmail\\nAPI\\nProcedure\\nMailing lists\\nStatus\\nWhois\\nDomain contact\\nReport abuse (abuse@ovh.net)\\nWHOIS information disclosure request\\nIntellectual property\\nTrademarks\\nSupport\\nHelp centre\\nGuides\\nLearning Centre\\nGlossary\\nCommunity\\nSupport levels\\nContact us\\nOVHcloud Customer Service\\nMonday to Friday, 9am to 6pm\\n+44333 370 0425\\nLocal call rate\\nNews\\nPress\\nBlog\\nSocial networks\\nKeep in touch\\n\\u00a9 Copyright 1999-2026 OVH SAS.\\nTerms & conditions\\nContracts\\nData protection\\nManage my cookies\\nRights and obligations of domain name holders\\nICANN documentation for domain name holders\\nPayments\\nSitemap\\nAbout OVHcloud\\nOVHcloud Careers\\nIn accordance with the 2006\\/112\\/CE Directive, modified on 01\\/01\\/2015, prices incl. 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The discipline broadly divides into three categories: supervised learning, where models train on labelled data to predict outcomes; unsupervised learning, which discovers hidden structures in unlabelled datasets; and reinforcement learning, where systems learn optimal actions through trial and reward mechanisms. These methodologies form the foundation for countless applications, from fraud detection systems that protect financial transactions to patient record analysis that enhances healthcare delivery.<\\/p><p>Cloud computing amplifies the potential of machine learning by addressing two critical challenges: scalability and cost efficiency. Traditional infrastructure often struggles to accommodate the fluctuating demands of machine learning workloads, which can require significant processing power during training phases and minimal resources during dormant periods. Cloud environments eliminate this constraint by offering elastic resources that scale dynamically with demand. Organisations can access high-performance virtual machines, specialised hardware such as cloud GPUs, and container orchestration tools like Kubernetes to manage complex deployments. This flexibility ensures that computational resources align precisely with project requirements, avoiding both underutilisation and bottlenecks.<\\/p><h3>Core Principles and Algorithms Deployed in the Cloud<\\/h3><p>The deployment of machine learning algorithms in cloud environments relies on understanding how different learning paradigms function. Supervised learning algorithms excel at tasks where historical data includes known outcomes, enabling applications such as predictive analytics for retail inventory management or customer behaviour forecasting. Retailers leverage these models to anticipate demand patterns, optimising stock levels and reducing waste. Meanwhile, unsupervised learning techniques uncover relationships within data that lack predefined labels, proving invaluable for segmentation tasks and anomaly detection. Financial services institutions deploy these methods to identify unusual transaction patterns that may indicate fraudulent activity.<\\/p><p>Reinforcement learning, though computationally intensive, finds its niche in scenarios requiring sequential decision-making and adaptation. Cloud platforms provide the necessary infrastructure to train these complex models, which learn through interaction with simulated or real environments. The availability of managed services such as machine learning notebooks, training environments, and deployment pipelines streamlines the entire workflow. Developers can experiment with different algorithms, fine-tune hyperparameters, and deploy production-ready models without managing underlying hardware complexities. Database solutions including MongoDB, MySQL, and PostgreSQL integrate seamlessly with these workflows, ensuring efficient data retrieval and storage throughout the machine learning lifecycle.<\\/p><h3>Scalability and Resource Management for ML Workloads<\\/h3><p>Effective resource management stands as a cornerstone of successful machine learning implementation in cloud settings. Training sophisticated models demands substantial processing power and memory, often requiring distributed computing approaches that span multiple virtual machines or bare metal servers. Cloud providers offer dedicated servers with configurations ranging from general-purpose to high-grade options optimised for intensive computational tasks. Virtual private servers provide a cost-effective alternative for smaller projects, delivering scalability and security without the overhead of full bare metal deployments.<\\/p><p>Storage considerations play an equally vital role, as machine learning projects generate and consume enormous volumes of data. Cloud platforms provide diverse storage solutions, from block storage for high-speed access to object storage for archival purposes and cold archive options for long-term retention. These services integrate with networking capabilities such as private networks and load balancers to ensure data flows efficiently between storage systems and compute resources. DDoS protection and SSL gateways safeguard sensitive information throughout the processing pipeline, addressing security concerns that often accompany cloud-based deployments.<\\/p><p>DevOps integration further enhances the operational efficiency of machine learning workloads. Containerisation technologies enable consistent environments across development, testing, and production stages, whilst orchestration platforms manage resource allocation and failover mechanisms. Managed infrastructure services reduce the burden on internal teams, allowing them to focus on model development and optimisation rather than system administration. This approach aligns with broader trends towards automation and agile methodologies, accelerating time-to-market for machine learning-powered applications.<\\/p><h2>Practical Applications and Real-World Use Cases<\\/h2><p>The practical applications of machine learning in cloud environments span virtually every industry, demonstrating the technology's versatility and impact. Healthcare organisations employ predictive analytics to forecast patient outcomes, identify at-risk populations, and personalise treatment plans. Machine learning models analyse electronic health records, imaging data, and genetic information to support clinical decision-making, improving both diagnostic accuracy and treatment efficacy. Cloud platforms enable healthcare providers to process sensitive patient data whilst maintaining compliance with regulations such as GDPR, HIPAA, and HDS certifications, which mandate stringent data protection measures.<\\/p><p>Retail businesses leverage machine learning for demand forecasting, recommendation engines, and dynamic pricing strategies. By analysing purchasing patterns, seasonal trends, and external factors such as weather or economic indicators, retailers optimise inventory levels and maximise revenue. Cloud-based analytics tools including Kafka and OpenSearch facilitate real-time data processing, allowing businesses to respond swiftly to market changes. The financial services sector similarly benefits from machine learning applications that detect fraudulent transactions, assess credit risk, and automate trading strategies. These systems process vast transaction volumes in real time, identifying anomalies that human analysts might overlook.<\\/p><h3>Industry-Specific Machine Learning Solutions on Cloud Platforms<\\/h3><p>Manufacturing industries harness machine learning for predictive maintenance, quality control, and supply chain optimisation. Sensors embedded in machinery generate continuous streams of data that algorithms analyse to predict equipment failures before they occur, minimising downtime and maintenance costs. Cloud platforms provide the computational resources necessary to process this sensor data at scale, integrating with IoT frameworks and edge computing solutions. Quality control systems employ computer vision algorithms to inspect products on assembly lines, identifying defects with greater consistency than manual inspection methods.<\\/p><p>The gaming industry relies on machine learning for player behaviour analysis, content personalisation, and anti-cheat systems. Cloud infrastructure supports the computational demands of these applications whilst accommodating spikes in player activity during peak hours or game launches. Blockchain initiatives utilise machine learning for fraud detection, smart contract optimisation, and market analysis, combining distributed ledger technology with predictive capabilities. Public sector organisations implement machine learning solutions for traffic management, resource allocation, and citizen service improvement, leveraging cloud platforms to handle sensitive data securely and maintain public trust.<\\/p><p>Educational institutions and research organisations benefit from cloud-based machine learning environments that democratise access to advanced computational tools. Startups and scaleups find particular value here, as cloud services eliminate the capital expenditure associated with building in-house infrastructure. Technology partners including Veeam, VMware, Nutanix, SAP, NetApp, and Nvidia collaborate with cloud providers to deliver integrated solutions that combine hardware, software, and expertise. This ecosystem approach ensures organisations can access best-of-breed technologies without navigating complex integration challenges independently.<\\/p><h3>Cost Optimisation and Performance Benefits for Businesses<\\/h3><p>Cost efficiency emerges as a primary driver for organisations adopting machine learning in cloud environments. Traditional on-premises deployments require significant upfront investment in hardware, facilities, and personnel, with resources often sitting idle during periods of low demand. Cloud computing transforms this model by shifting expenses from capital to operational budgets, allowing organisations to pay only for resources consumed. Virtual machine instances, metal instances, and managed services offer flexible pricing structures that align costs with actual usage, improving financial predictability and resource allocation.<\\/p><p>Performance benefits extend beyond mere computational power, encompassing reduced latency, improved reliability, and enhanced collaboration capabilities. Cloud platforms distribute resources across multiple availability zones, ensuring high availability and disaster recovery capabilities without requiring organisations to maintain duplicate infrastructure. Backup services and cold archive solutions protect against data loss whilst maintaining cost efficiency. Networking features such as additional IP addresses, load balancers, and content delivery networks optimise application performance for geographically distributed user bases.<\\/p><p>The integration of machine learning with cloud infrastructure facilitates continuous improvement through automated model retraining and deployment pipelines. As new data becomes available, algorithms can retrain automatically, incorporating fresh insights without manual intervention. This capability proves particularly valuable in dynamic environments where patterns shift rapidly, such as financial markets or cybersecurity threat landscapes. Compliance considerations remain paramount throughout these processes, with cloud providers maintaining certifications including ISO standards, PCI DSS, and SecNumCloud to assure customers of rigorous security and governance practices.<\\/p><p>Organisations embarking on cloud migration or hybrid cloud strategies find machine learning workloads well-suited to these transitions. App modernisation initiatives often incorporate machine learning capabilities to enhance legacy systems with predictive features and intelligent automation. Domain hosting services, email solutions, and collaborative platforms integrate with machine learning APIs to deliver smarter communication tools and enhanced productivity. The convergence of these technologies creates opportunities for innovation that were previously accessible only to organisations with substantial resources, levelling the playing field and fostering competition across sectors.<\\/p>\"}"
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