{"product_id":"operationalize-machine-learning-and-generative-ai-solutions-ai-300","title":"Operationalize Machine Learning and Generative AI Solutions (AI-300)","description":"\u003cdiv\u003e\n\u003cp\u003eThis course prepares learners to design, implement, and operate Machine Learning Operations (MLOps) and Generative AI Operations (GenAIOps) solutions on Azure. It covers building secure and scalable AI infrastructure, managing the full lifecycle of traditional machine learning models with Azure Machine Learning, and deploying, evaluating, monitoring, and optimizing generative AI applications and agents using Microsoft Foundry.\u003c\/p\u003e\r\n\u003cp\u003eLearners will gain hands-on knowledge of automation, continuous integration and delivery, infrastructure as code, and observability by using tools such as GitHub Actions, Azure CLI, and Bicep. The course emphasizes collaboration with data science and DevOps teams to deliver reliable, production-ready AI systems aligned with modern MLOps and GenAIOps best practices.\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch3\u003eOperationalize Machine Learning and Generative AI Solutions (AI-300) Benefits\u003c\/h3\u003e\n\u003cul\u003e\u003cli\u003e\n\u003cp\u003e\u003cb\u003eCourse Benefits\u003c\/b\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eDesign and run machine learning experiments using Azure Machine Learning, including AutoML and model tracking\u003c\/li\u003e\n\u003cli\u003eOptimize model performance through hyperparameter tuning and structured experimentation\u003c\/li\u003e\n\u003cli\u003eBuild and automate end-to-end ML workflows using pipelines and CI\/CD with GitHub Actions\u003c\/li\u003e\n\u003cli\u003eDeploy, test, and manage machine learning models in production environments\u003c\/li\u003e\n\u003cli\u003eImplement MLOps practices to improve reliability, scalability, and repeatability of AI solutions\u003c\/li\u003e\n\u003cli\u003eApply GenAIOps principles to develop and manage generative AI applications using Microsoft Foundry\u003c\/li\u003e\n\u003cli\u003eManage prompts and AI agents as version-controlled assets using Git-based workflows\u003c\/li\u003e\n\u003cli\u003eEvaluate and optimize AI models and agents using structured metrics for quality, cost, and performance\u003c\/li\u003e\n\u003cli\u003eAutomate AI evaluation processes to ensure continuous improvement and consistency\u003c\/li\u003e\n\u003cli\u003eMonitor AI application performance, including latency, usage, and cost\u003c\/li\u003e\n\u003cli\u003eAnalyze and debug AI systems using tracing and observability techniques to improve reliability\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003ePrerequisites\u003c\/b\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eWorking knowledge of Python or R programming\u003c\/li\u003e\n\u003cli\u003eExperience developing and training machine learning models\u003c\/li\u003e\n\u003cli\u003eFamiliarity with Azure Machine Learning concepts and workflows\u003c\/li\u003e\n\u003cli\u003eUnderstanding of core generative AI concepts and Azure AI services\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eExam Information\u003c\/b\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/learn.microsoft.com\/en-us\/credentials\/certifications\/operationalizing-machine-learning-and-generative-ai-solutions\/?practice-assessment-type=certification\"\u003eMicrosoft Certified: Machine Learning Operations (MLOps) Engineer Associate (beta) - Certifications | Microsoft Learn\u003c\/a\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eWho should attend:\u003c\/b\u003e\u003c\/p\u003e\n\u003cdiv\u003eThis course is intended for experienced data scientists, machine learning engineers, and DevOps professionals responsible for designing, deploying, and operating enterprise AI solutions on Azure. It is well suited for learners with professional experience in Python, a working understanding of machine learning fundamentals, and familiarity with modern DevOps practices. Participants will benefit most if they are preparing to operationalize MLOps and GenAIOps workflows using Azure-native services in production environments.\u003c\/div\u003e\n\u003c\/li\u003e\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\u003ch3\u003eMLOps and GenAI on Azure Training Outline\u003c\/h3\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eLearning Objectives\u003c\/h4\u003e\n\u003cp\u003e\u003cb\u003eExperiment with Azure Machine Learning\u003c\/b\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eIntroduction\u003c\/li\u003e\n\u003cli\u003ePreprocess data and configure featurization\u003c\/li\u003e\n\u003cli\u003eRun an automated machine learning experiment\u003c\/li\u003e\n\u003cli\u003eEvaluate and compare models\u003c\/li\u003e\n\u003cli\u003eConfigure MLflow for model tracking in notebooks\u003c\/li\u003e\n\u003cli\u003eTrain and track models in notebooks\u003c\/li\u003e\n\u003cli\u003eEvaluate models with the Responsible AI dashboard\u003c\/li\u003e\n\u003cli\u003eExercise: Find the best classification model with Azure Machine Learning\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cb\u003ePerform Hyperparameter Tuning with Azure Machine Learning\u003c\/b\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eIntroduction\u003c\/li\u003e\n\u003cli\u003eDefine a search space\u003c\/li\u003e\n\u003cli\u003eConfigure a sampling method\u003c\/li\u003e\n\u003cli\u003eConfigure early termination\u003c\/li\u003e\n\u003cli\u003eUse a sweep job for hyperparameter tuning\u003c\/li\u003e\n\u003cli\u003eExercise: Run a sweep job\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cb\u003eRun Pipelines in Azure Machine Learning\u003c\/b\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eIntroduction\u003c\/li\u003e\n\u003cli\u003eCreate components\u003c\/li\u003e\n\u003cli\u003eCreate a pipeline\u003c\/li\u003e\n\u003cli\u003eRun a pipeline job\u003c\/li\u003e\n\u003cli\u003eExercise: Run a pipeline job\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cb\u003eTrigger Azure Machine Learning Jobs with GitHub Actions\u003c\/b\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eIntroduction\u003c\/li\u003e\n\u003cli\u003eUnderstand the business problem\u003c\/li\u003e\n\u003cli\u003eExplore the solution architecture\u003c\/li\u003e\n\u003cli\u003eUse GitHub Actions for model training\u003c\/li\u003e\n\u003cli\u003eExercise\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cb\u003eTrigger GitHub Actions with Feature-Based Development\u003c\/b\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eIntroduction\u003c\/li\u003e\n\u003cli\u003eUnderstand the business problem\u003c\/li\u003e\n\u003cli\u003eExplore the solution architecture\u003c\/li\u003e\n\u003cli\u003eTrigger a workflow\u003c\/li\u003e\n\u003cli\u003eExercise\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cb\u003eWork with Environments in GitHub Actions\u003c\/b\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eIntroduction\u003c\/li\u003e\n\u003cli\u003eUnderstand the business problem\u003c\/li\u003e\n\u003cli\u003eExplore the solution architecture\u003c\/li\u003e\n\u003cli\u003eSet up environments\u003c\/li\u003e\n\u003cli\u003eExercise\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cb\u003eDeploy a Model with GitHub Actions\u003c\/b\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eIntroduction\u003c\/li\u003e\n\u003cli\u003eUnderstand the business problem\u003c\/li\u003e\n\u003cli\u003eExplore the solution architecture\u003c\/li\u003e\n\u003cli\u003eModel deployment\u003c\/li\u003e\n\u003cli\u003eExercise\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cb\u003ePlan and Prepare a GenAIOps Solution\u003c\/b\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eIntroduction\u003c\/li\u003e\n\u003cli\u003eExplore use cases for GenAIOps\u003c\/li\u003e\n\u003cli\u003eSelect the right generative AI model\u003c\/li\u003e\n\u003cli\u003eUnderstand the development lifecycle of a language model application\u003c\/li\u003e\n\u003cli\u003eExplore available tools and frameworks to implement GenAIOps\u003c\/li\u003e\n\u003cli\u003eExercise: Compare language models from the model catalog\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cb\u003eManage Prompts for Agents in Microsoft Foundry with GitHub\u003c\/b\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eIntroduction\u003c\/li\u003e\n\u003cli\u003eApply version control to prompts\u003c\/li\u003e\n\u003cli\u003eUnderstand Microsoft Foundry agents and prompt versioning\u003c\/li\u003e\n\u003cli\u003eOrganize prompts in GitHub repositories\u003c\/li\u003e\n\u003cli\u003eDevelop safe prompt deployment workflows\u003c\/li\u003e\n\u003cli\u003eExercise: Develop prompt and agent versions\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cb\u003eEvaluate and Optimize AI Agents Through Structured Experiments\u003c\/b\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eIntroduction\u003c\/li\u003e\n\u003cli\u003eDesign evaluation experiments\u003c\/li\u003e\n\u003cli\u003eApply Git-based workflows to optimization experiments\u003c\/li\u003e\n\u003cli\u003eApply evaluation rubrics for consistent scoring\u003c\/li\u003e\n\u003cli\u003eExercise: Evaluate and compare AI agent versions\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cb\u003eAutomate AI Evaluations with Microsoft Foundry and GitHub Actions\u003c\/b\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eIntroduction\u003c\/li\u003e\n\u003cli\u003eUnderstand why automated evaluations matter\u003c\/li\u003e\n\u003cli\u003eAlign evaluators with human criteria\u003c\/li\u003e\n\u003cli\u003eCreate evaluation datasets\u003c\/li\u003e\n\u003cli\u003eImplement batch evaluations with Python\u003c\/li\u003e\n\u003cli\u003eIntegrate evaluations into GitHub Actions\u003c\/li\u003e\n\u003cli\u003eExercise: Set up automated evaluations\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cb\u003eMonitor Your Generative AI Application\u003c\/b\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eIntroduction\u003c\/li\u003e\n\u003cli\u003eWhy monitoring matters\u003c\/li\u003e\n\u003cli\u003eUnderstand key metrics to monitor\u003c\/li\u003e\n\u003cli\u003eExplore monitoring with Azure\u003c\/li\u003e\n\u003cli\u003eIntegrate monitoring into your application\u003c\/li\u003e\n\u003cli\u003eInterpret monitoring results\u003c\/li\u003e\n\u003cli\u003eExercise: Enable monitoring for a generative AI application\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cb\u003eAnalyze and Debug Your Generative AI Application with Tracing\u003c\/b\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eIntroduction\u003c\/li\u003e\n\u003cli\u003eWhy tracing is important\u003c\/li\u003e\n\u003cli\u003eIdentify what to trace in generative AI applications\u003c\/li\u003e\n\u003cli\u003eImplement tracing in generative AI applications\u003c\/li\u003e\n\u003cli\u003eDebug complex workflows with advanced tracing patterns\u003c\/li\u003e\n\u003cli\u003eAnalyze trace data to inform decisions\u003c\/li\u003e\n\u003cli\u003eExercise: Enable tracing for a generative AI application\u003cb\u003e \u003c\/b\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e","brand":"Microsoft","offers":[{"title":"267D94US \/ 2026-07-07T09:00:00 \/ Herndon, VA","offer_id":48833430487259,"sku":"US-8770-IL","price":2512.0,"currency_code":"USD","in_stock":true},{"title":"268B11CN \/ 2026-08-18T09:00:00 \/ Ottawa","offer_id":48833430520027,"sku":"US-8770-IL","price":2512.0,"currency_code":"USD","in_stock":true},{"title":"26BA67CN \/ 2026-11-17T09:00:00 \/ Ottawa","offer_id":48833430552795,"sku":"US-8770-IL","price":2512.0,"currency_code":"USD","in_stock":true},{"title":"271C82US \/ 2027-01-05T09:00:00 \/ Herndon, 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