Operationalize Machine Learning and Generative AI solutions (AI-300T00)

AI-300T00: Operationalize Machine Learning and Generative AI Solutions course is designed to help AI engineers, data scientists, and DevOps professionals implement scalable and production-ready AI systems on Microsoft Azure. The course covers the complete lifecycle of machine learning operations (MLOps) and generative AI operations (GenAIOps), including model experimentation, automated training, CI/CD pipelines, deployment, monitoring, and optimization.

Retail Price: $2,495.00

Next Date: 06/09/2026

Course Days: 4


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Overview

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.

Audience Profile

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.

Learning Objectives

  • Implement end-to-end MLOps workflows using Azure Machine Learning.
  • Automate model training, experimentation, and hyperparameter tuning.
  • Build and manage machine learning pipelines for scalable workflows.
  • Integrate GitHub Actions to enable CI/CD automation for ML models.
  • Deploy, monitor, and manage machine learning and generative AI applications in production.
  • Evaluate, optimize, and debug AI systems using GenAIOps practices, monitoring, and tracing tools.

  1. Operationalize machine learning models (MLOps)
  2. Experiment with Azure Machine Learning
    1. Introduction
    2. Preprocess data and configure featurization
    3. Run an automated machine learning experiment
    4. Evaluate and compare models
    5. Configure MLflow for model tracking in notebooks
    6. Train and track models in notebooks
    7. Evaluate models with the Responsible AI dashboard
    8. Exercise – Find the best classification model with Azure Machine Learning
  3. Perform hyperparameter tuning with Azure Machine Learning
    1. Introduction
    2. Define a search space
    3. Configure a sampling method
    4. Configure early termination
    5. Use a sweep job for hyperparameter tuning
    6. Exercise – Run a sweep job
  4. Run pipelines in Azure Machine Learning
    1. Introduction
    2. Create components
    3. Create a pipeline
    4. Run a pipeline job
    5. Exercise – Run a pipeline job
  5. Trigger Azure Machine Learning jobs with GitHub Actions
    1. Introduction
    2. Understand the business problem
    3. Explore the solution architecture
    4. Use GitHub Actions for model training
    5. Exercise
  6. Trigger GitHub Actions with feature-based development
    1. Introduction
    2. Understand the business problem
    3. Explore the solution architecture
    4. Trigger a workflow
    5. Exercise
  7. Work with environments in GitHub Actions
    1. Introduction
    2. Understand the business problem
    3. Explore the solution architecture
    4. Set up environments
    5. Exercise
  8. Deploy a model with GitHub Actions
    1. Introduction
    2. Understand the business problem
    3. Explore the solution architecture
    4. Model deployment
    5. Exercise
  9. Operationalize generative AI applications (GenAIOps)
  10. Plan and prepare a GenAIOps solution
  11. Introduction
  12. Explore use cases for GenAIOps
  13. Select the right generative AI model
  14. Understand the development lifecycle of a language model application
  15. Explore available tools and frameworks to implement GenAIOps
  16. Exercise – Compare language models from the model catalog
  17. Manage prompts for agents in Microsoft Foundry with GitHub
  18. Introduction
  19. Apply version control to prompts
  20. Understand Microsoft Foundry agents and prompt versioning
  21. Organize prompts in GitHub repositories
  22. Develop safe prompt deployment workflows
  23. Exercise – Develop prompt and agent versions
  24. Evaluate and optimize AI agents through structured experiments
  25. Introduction
  26. Design evaluation experiments
  27. Apply Git-based workflows to optimization experiments
  28. Apply evaluation rubrics for consistent scoring
  29. Exercise – Evaluate and compare AI agent versions
  30. Automate AI evaluations with Microsoft Foundry and GitHub Actions
  31. Introduction
  32. Understand why automated evaluations matter
  33. Align evaluators with human criteria
  34. Create evaluation datasets
  35. Implement batch evaluations with Python
  36. Integrate evaluations into GitHub Actions
  37. Exercise – Set up automated evaluations
  38. Monitor your generative AI application
  39. Introduction
  40. Why do you need to monitor?
  41. Understand key metrics to monitor
  42. Explore how to monitor with Azure
  43. Integrate monitoring into your app
  44. Interpret monitoring results
  45. Exercise – Enable monitoring for a generative AI application
  46. Analyze and debug your generative AI app with tracing
  47. Introduction
  48. Why do you need to use tracing?
  49. Identify what to trace in generative AI applications
  50. Implement tracing in generative AI applications
  51. Debug complex workflows with advanced tracing patterns
  52. Make informed decisions with trace data analysis
  53. Exercise – Enable tracing for a generative AI application
Course Dates Course Times (EST) Delivery Mode GTR
6/9/2026 - 6/12/2026 10:00 AM - 6:00 PM Virtual Enroll
7/14/2026 - 7/17/2026 10:00 AM - 6:00 PM Virtual Enroll
7/28/2026 - 7/31/2026 10:00 AM - 6:00 PM Virtual Enroll