Cloud Private Public

Operationalize machine learning and generative AI solutions (AI-300T00)

4 days

Learn to design, implement, and operate MLOps and GenAIOps solutions on Azure with Azure Machine Learning, Microsoft Foundry, GitHub Actions, and Bicep.

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Course Overview

This course prepares you to design, implement, and operate Machine Learning Operations (MLOps) and Generative AI Operations (GenAIOps) solutions on Azure. You will build secure, scalable AI infrastructure; manage the end-to-end lifecycle of traditional machine learning models with Azure Machine Learning; and deploy, evaluate, monitor, and optimize generative AI applications and agents using Microsoft Foundry.

You will also work with automation, CI/CD, infrastructure as code, and observability using tools such as GitHub Actions, Azure CLI, and Bicep. The course emphasizes collaboration across data science and DevOps teams to deliver reliable, production-ready AI systems.

Course Benefits

Design and operate MLOps workflows using Azure Machine Learning
Run machine learning experiments, track models with MLflow, and evaluate models with the Responsible AI dashboard
Perform hyperparameter tuning using search spaces, sampling methods, early termination, and sweep jobs
Build and run Azure Machine Learning pipelines using reusable components
Automate model training and deployment workflows with GitHub Actions
Plan GenAIOps solutions for generative AI applications and agents using Microsoft Foundry
Manage prompts with Git-based version control and safe deployment workflows
Evaluate and optimize AI agents using structured experiments, scoring rubrics, and automated evaluations
Monitor, trace, analyze, and troubleshoot generative AI applications on Azure

Delivery Methods

Public Class
Live expert-led online training from anywhere. Guaranteed to run .

Private Class
Delivered for your team at your site or online.

Microsoft Certified Partner

Webucator is a Microsoft Certified Partner. This class uses official Microsoft courseware and will be delivered by a Microsoft Certified Trainer (MCT).

Course Outline

Experiment with Azure Machine Learning
1. Preprocess data and configure featurization
2. Run an automated machine learning experiment
3. Evaluate and compare models
4. Configure MLflow for model tracking in notebooks
5. Train and track models in notebooks
6. Evaluate models with the Responsible AI dashboard
7. Module assessment
Perform hyperparameter tuning with Azure Machine Learning
1. Define a search space
2. Configure a sampling method
3. Configure early termination
4. Use a sweep job for hyperparameter tuning
5. Module assessment
Run pipelines in Azure Machine Learning
1. Create components
2. Create a pipeline
3. Run a pipeline job
4. Module assessment
Trigger Azure Machine Learning jobs with GitHub Actions
1. Understand the business problem
2. Explore the solution architecture
3. Use GitHub Actions for model training
4. Module assessment
Trigger GitHub Actions with feature-based development
1. Understand the business problem
2. Explore the solution architecture
3. Trigger a workflow
4. Module assessment
Work with environments in GitHub Actions
1. Understand the business problem
2. Explore the solution architecture
3. Set up environments
4. Module assessment
Deploy a model with GitHub Actions
1. Understand the business problem
2. Explore the solution architecture
3. Deploy a model
4. Module assessment
Plan and prepare a GenAIOps solution
1. Explore use cases for GenAIOps
2. Select the right generative AI model
3. Understand the development lifecycle of a language model application
4. Explore available tools and frameworks for implementing GenAIOps
5. Module assessment
Manage prompts for agents in Microsoft Foundry with GitHub
1. Apply version control to prompts
2. Understand Microsoft Foundry agents and prompt versioning
3. Organize prompts in GitHub repositories
4. Develop safe prompt deployment workflows
Evaluate and optimize AI agents through structured experiments
1. Design evaluation experiments
2. Apply Git-based workflows to optimization experiments
3. Apply evaluation rubrics for consistent scoring
Automate AI evaluations with Microsoft Foundry and GitHub Actions
1. Understand why automated evaluations matter
2. Align evaluators with human criteria
3. Create evaluation datasets
4. Implement batch evaluations with Python
5. Integrate evaluations into GitHub Actions
Monitor your generative AI application
1. Understand the need for monitoring
2. Understand key metrics to monitor
3. Explore monitoring with Azure
4. Integrate monitoring into your application
5. Interpret monitoring results
Analyze and debug your generative AI application with tracing
1. Understand the need for tracing
2. Identify what to trace in generative AI applications
3. Implement tracing in generative AI applications
4. Debug complex workflows with advanced tracing patterns
5. Make informed decisions through trace data analysis

Class Materials

Each student receives a comprehensive set of materials, including course notes and all class examples.

Class Prerequisites

Experience in the following would be useful for this Azure class:

Experience with Python
A foundational understanding of machine learning concepts
Basic familiarity with DevOps practices such as source control, CI/CD, and command-line tools

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