Learn how to find the best machine learning model with automated machine learning (AutoML), MLflow-tracked notebooks, and the Responsible AI dashboard.
In this module, you learn how to:
- Prepare your data to use AutoML for classification.
- Configure and run an AutoML experiment.
- Evaluate and compare AutoML models.
- Configure MLflow for model tracking in notebooks.
- Use MLflow for model tracking in notebooks.
- Evaluate a trained model using the Responsible AI dashboard.
Learn how to perform hyperparameter tuning with a sweep job in Azure Machine Learning.
In this module, you learn how to:
- Define a hyperparameter search space.
- Configure hyperparameter sampling.
- Select an early-termination policy.
- Run a sweep job.
Learn how to create and use components to build pipeline in Azure Machine Learning. Run and schedule Azure Machine Learning pipelines to automate machine learning workflows.
In this module, you'll learn how to:
- Create components.
- Build an Azure Machine Learning pipeline.
- Run an Azure Machine Learning pipeline.
MLOps, machine learning operations
In this module, you'll learn how to:
- Create and assign a service principal the permissions needed to run an Azure Machine Learning job.
- Store Azure credentials securely using secrets in GitHub Secrets.
- Create a GitHub Action using YAML that uses the stored Azure credentials to run an Azure Machine Learning job.
Learn how to trigger GitHub Actions with feature-based development to achieve machine learning operations or MLOps.
In this module, you'll learn how to:
- Work with feature-based development.
- Protect the main branch.
- Trigger a GitHub Actions workflow by merging a pull request.
machine learning operations, MLOps, environments
In this module, you'll learn how to:
- Set up environments in GitHub.
- Use environments in GitHub Actions.
- Add approval gates to assign required reviewers before moving the model to the next environment.
machine learning operations, MLOps, model deployment, online endpoint
In this module, you'll learn how to:
- Deploy a model to a managed endpoint.
- Trigger model deployment with GitHub Actions.
- Test the deployed model.

Syllabus

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 assessment
- Summary
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 assessment
- Summary
Run pipelines in Azure Machine Learning
- Introduction
- Create components
- Create a pipeline
- Run a pipeline job
- Exercise - Run a pipeline job
- Module assessment
- Summary
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 assessment
- Summary
Trigger GitHub Actions with feature-based development
- Introduction
- Understand the business problem
- Explore the solution architecture
- Trigger a workflow
- Exercise
- Module assessment
- Summary
Work with environments in GitHub Actions
- Introduction
- Understand the business problem
- Explore the solution architecture
- Set up environments
- Exercise
- Module assessment
- Summary
Deploy a model with GitHub Actions
- Introduction
- Understand the business problem
- Explore the solution architecture
- Model deployment
- Exercise
- Module assessment
- Summary