Course Outline
1 - 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
- Module assessment
2 - 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
- Module assessment
3 - Run pipelines in Azure Machine Learning
- Create components
- Create a pipeline
- Run a pipeline job
- Module assessment
4 - Trigger Azure Machine Learning jobs with GitHub Actions
- Understand the business problem
- Explore the solution architecture
- Use GitHub Actions for model training
- Module assessment
5 - Trigger GitHub Actions with feature-based development
- Understand the business problem
- Explore the solution architecture
- Trigger a workflow
- Module assessment
6 - Work with environments in GitHub Actions
- Understand the business problem
- Explore the solution architecture
- Set up environments
- Module assessment
7 - Deploy a model with GitHub Actions
- Understand the business problem
- Explore the solution architecture
- Model deployment
- Module assessment
8 - Plan and prepare a GenAIOps solution
- Explore use cases for GenAIOps
- Select the right generative AI model
- Understand the development lifecycle of a language model application
- Explore available tools and frameworks to implement GenAIOps
- Module assessment
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
Target Audience
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.