Train models in Azure Machine Learning with the CLI (v2)

In this module, you'll learn how to:

• Install the Azure CLI and the Azure Machine Learning extension.
• Create an Azure Machine Learning workspace.
• Manage assets in the Azure Machine Learning workspace.

In this module, you'll learn how to:

• Train a model with a Python script using the CLI (v2).
• Perform hyperparameter tuning with the CLI (v2).

In this module, you'll learn how to:

• Automatically track model metrics with MLflow when using common machine learning libraries.
• Track custom metrics with MLflow.
• Use MLflow model assets to register a model in the Azure Machine Learning workspace.

In this module, you'll learn how to:

• Understand managed online endpoints.
• Understand how to use managed endpoint with blue/green deployments.
• Deploy a MLflow model to a managed online endpoint.

• Introduction
• Use the Azure CLI (v2) with Azure Machine Learning
• Create an Azure Machine Learning workspace with CLI (v2)
• Manage workspace assets with CLI (v2)
• Exercise: Create an Azure Machine Learning workspace
• Knowledge check
• Summary

• Introduction
• Run a Python script as a training job with CLI (v2)
• Exercise: Create a basic training job
• Run a hyperparameter tuning job with CLI (v2)
• Exercise: Run a sweep job
• Knowledge check
• Summary

• Introduction
• Track and view model metrics with MLflow
• Manage models with MLflow
• Exercise: Train and track model with MLflow
• Knowledge check
• Summary

• Introduction
• Explore managed online endpoints
• Deploy your model to a managed endpoint
• Exercise: Deploy your model with CLI (v2)
• Knowledge check
• Summary