This 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. Learners will gain 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.
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Course Outline
1. Get started with machine learning in Azure
• Define the problem
• Get and prepare data
• Train the model
• Use Azure Machine Learning studio
• Integrate a model
2. Experiment with Azure Machine Learning
• Preprocess data and configure featurization
• Run an automated machine learning experiment
• Evaluate and compare models
• Configure MLflow for model tracking in notebooks
• Train and track models in notebooks
• Evaluate models with the Responsible AI dashboard
3. Run training scripts and track models with MLflow in Azure Machine Learning
• Convert a notebook to a script
• Run a script as a command job
• Use parameters in a command job
• Track metrics with MLflow
• View metrics and evaluate models
4. Perform hyperparameter tuning with Azure Machine Learning
• Define a search space
• Configure a sampling method
• Configure early termination
• Use a sweep job for hyperparameter tuning
5. Run pipelines in Azure Machine Learning
• Create components
• Create a pipeline
• Run a pipeline job
6. Automate model training with GitHub Actions
• Use source control for machine learning assets
• Apply trunk-based development
• Validate changes with GitHub Actions
• Connect GitHub Actions to Azure Machine Learning securely
• Trigger Azure Machine Learning jobs and pipelines
7. Deploy and monitor a model in Azure Machine Learning
• Prepare a model for deployment
• Explore the deployment and monitoring architecture
• Control deployments with GitHub environments
• Deploy, promote, and roll back a model with GitHub Actions
• Monitor the deployed model
8. Plan and prepare a GenAIOps solution
• Define agent specifications
• Design and optimize your agent
• Explore the GenAIOps lifecycle
• Explore available tools and frameworks to implement GenAIOps
9. Manage prompts for agents in Microsoft Foundry with GitHub
• Apply version control to prompts
• Understand Microsoft Foundry agents and prompt versioning
• Organize prompts in GitHub repositories
• Develop safe prompt deployment workflows
10. Evaluate and optimize AI agents through structured experiments
• Design evaluation experiments
• Apply Git-based workflows to optimization experiments
• Apply evaluation rubrics for consistent scoring
11. Automate AI evaluations with Microsoft Foundry and GitHub Actions
• Understand why automated evaluations matter
• Align evaluators with human criteria
• Create evaluation datasets
• Implement batch evaluations with Python
• Integrate evaluations into GitHub Actions
12. Monitor your generative AI application
• Why do you need to monitor?
• Understand key metrics to monitor
• Explore how to monitor with Azure
• Integrate monitoring into your app
• Interpret monitoring results
13. Analyze and debug your generative AI app with tracing
• Why do you need to use tracing?
• Identify what to trace in generative AI applications
• Implement tracing in generative AI applications
• Debug complex workflows with advanced tracing patterns
• Make informed decisions with trace data analysis
Course Details
• Programming experience with Python or R.
• Experience developing and training machine learning models.
• Familiarity with basic Azure Machine Learning concepts.
• Familiarity with fundamental generative AI concepts and services in Azure. Consider completing the Microsoft Azure AI Fundamentals: Generative AI learning path first.
This course is intended for data scientists, machine learning engineers, and DevOps professionals who want to design and operate production-grade AI solutions on Azure. It is suited for learners with experience in Python, a foundational understanding of machine learning concepts, and basic familiarity with DevOps practices such as source control, CI/CD, and command-line tools, who are preparing to implement MLOps and GenAIOps workflows using Azure-native services.
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