AI-300 Operationalize AI Solutions Certification Training

Learning Path 1- Operationalize Machine Learning Models (MLOps)
• Module 1: Experiment with Azure Machine Learning
  • Introduction
  • 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
  • Exercise – Find the best classification model with Azure Machine Learning
• Module 2: Perform hyperparameter tuning with Azure Machine Learning
  • Introduction
  • Define a search space
  • Configure a sampling method
  • Configure early termination
  • Use a sweep job for hyperparameter tuning
  • Exercise – Run a sweep job
• Module 3: Run pipelines in Azure Machine Learning
  • Introduction
  • Create components
  • Create a pipeline
  • Run a pipeline job
  • Exercise – Run a pipeline job
• Module 4: Trigger Azure Machine Learning jobs with GitHub Actions
  • Introduction
  • Understand the business problem
  • Explore the solution architecture
  • Use GitHub Actions for model training
  • Exercise
• Module 5: Trigger GitHub Actions with feature-based development
  • Introduction
  • Understand the business problem
  • Explore the solution architecture
  • Trigger a workflow
  • Exercise
• Module 6: Work with environments in GitHub Actions
  • Introduction
  • Understand the business problem
  • Explore the solution architecture
  • Set up environments
  • Exercise
• Module 7: Deploy a model with GitHub Actions
  • Introduction
  • Understand the business problem
  • Explore the solution architecture
  • Model deployment
  • Exercise

Learning Path 2- Operationalize Generative AI Applications (GenAIOps)

• Module 1: Plan and prepare a GenAIOps solution
  • Introduction
  • 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
  • Exercise – Compare language models from the model catalog
• Module 2: Manage prompts for agents in Microsoft Foundry with GitHub
  • Introduction
  • Apply version control to prompts
  • Understand Microsoft Foundry agents and prompt versioning
  • Organize prompts in GitHub repositories
  • Develop safe prompt deployment workflows
  • Exercise – Develop prompt and agent versions
• Module 3: Evaluate and optimize AI agents through structured experiments
  • Introduction
  • Design evaluation experiments
  • Apply Git-based workflows to optimization experiments
  • Apply evaluation rubrics for consistent scoring
  • Exercise – Evaluate and compare AI agent versions
• Module 4: Automate AI evaluations with Microsoft Foundry and GitHub Actions
  • Introduction
  • Understand why automated evaluations matter
  • Align evaluators with human criteria
  • Create evaluation datasets
  • Implement batch evaluations with Python
  • Integrate evaluations into GitHub Actions
  • Exercise Set up automated evaluations
• Module 5: Monitor your generative AI application
  • Introduction
  • 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
  • Exercise Enable monitoring for a generative AI application
• Module 6: Analyze and debug your generative AI app with tracing
  • Introduction
  • 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
  • Exercise Enable tracing for a generative AI application

• AI/ML Practitioners
• AI/ML Consultants
• Data Scientists
• AI Research Scientists
• Solution Architects
• Data Analysts
• Business Analysts
• DevOps Engineers
• Cloud Engineers
• AI/ML Engineers
• Software Developers
• IT Project Managers
• Technical Managers
• Product Managers
• Researchers in AI Technology
• University Students in Tech Fields

Experience with Python or R, experience developing and training machine learning models, familiarity with basic Azure Machine Learning concepts, and basic DevOps knowledge, such as source control, CI/CD, and command-line tools.

After completing this training, you will be able to:

• Experiment, train, and evaluate machine learning models using Azure ML.
• Perform hyperparameter tuning and run ML pipelines with automation.
• Deploy ML models and generative AI apps in production with GitHub Actions.
• Operationalize GenAIOps solutions, including prompt management, evaluation, and monitoring.
• Integrate AI solutions with Microsoft Foundry, Azure services, and DevOps workflows.
• Implement responsible AI practices, model tracing, and governance in production.
• Monitor and debug AI applications using tracing, metrics, and logs.