Course AI-300T00 Operationalize machine learning and generative AI solutions
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 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.
After completing this course, students will be able to:
- Train and evaluate machine learning classification models using Azure Machine Learning
- Optimize model training with scripts, MLflow tracking, hyperparameter tuning, and ML pipelines
- Design and automate MLOps solutions using GitHub Actions and Azure Machine Learning
- Deploy and monitor ML models to a managed online endpoint in Azure Machine Learning
- Set up and configure Microsoft Foundry for generative AI application development
- Develop, version, and deploy AI agents using prompt engineering in Microsoft Foundry
- Systematically test, evaluate, and optimize AI agent prompts using automated cloud evaluators
- Monitor and trace generative AI agents in production using Application Insights and distributed tracing
- Optimize AI agents through fine-tuning, including supervised fine-tuning, reinforcement fine-tuning, and direct preference optimization
Audience Profile
This course is intended for data scientists, MLOps dngineers and AI developers who design and implement AI solutions using Azure Machine Learning and Microsoft Foundry. Students should be comfortable working with Python, cloud services, and GitHub. Typical candidates are building or maintaining machine learning pipelines, automating AI workflows, or operationalizing generative AI applications in enterprise environments.
Before attending this course, students must have:
- Familiarity with Python programming
- Experience with Azure services (Azure portal, subscriptions, resource groups)
- Familiarity with GitHub and version control
- Familiarity with basic Azure Machine Learning concepts
Experience with Azure Machine Learning or Azure AI services is beneficial but not required
1. Operationalize machine learning models (MLOps)
1.1 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
1.2 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
1.3 Run pipelines in Azure Machine Learning
- Create components
- Create a pipeline
- Run a pipeline job
1.4 Trigger Azure Machine Learning jobs with GitHub Actions
- Understand the business problem
- Explore the solution architecture
- Use GitHub Actions for model training
1.5 Trigger GitHub Actions with feature-based development
- Understand the business problem
- Explore the solution architecture
- Trigger a workflow
1.6 Work with environments in GitHub Actions
- Understand the business problem
- Explore the solution architecture
- Set up environments
1.7 Deploy a model with GitHub Actions
- Understand the business problem
- Explore the solution architecture
- Model deployment
2. Operationalize generative AI applications (GenAIOps)
2.1 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
2.2 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
2.3 Evaluate and optimize AI agents through structured experiments
- Design evaluation experiments
- Apply Git-based workflows to optimization experiments
- Apply evaluation rubrics for consistent scoring
2.4 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
2.5 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
2.6Analyze 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
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