Overview

Module 1: Learn about how to connect to data from the Azure Machine Learning workspace. You'll be introduced to datastores and data assets.
In this module, you'll learn how to:
- Work with Uniform Resource Identifiers (URIs).
- Create and use datastores.
- Create and use data assets.
Module 2: Azure Machine Learning Python SDK v2
In this module, you'll learn how to:
- Choose the appropriate compute target.
- Create and use a compute instance.
- Create and use a compute cluster.
Module 3: Use Azure Machine Learning Python SDK v2 to work with environments
In this module, you'll learn how to:
- Understand environments in Azure Machine Learning.
- Explore and use curated environments.
- Create and use custom environments.
Module 4: Run a command job with the Azure Machine Learning Python SDK v2.
In this module, you'll learn how to:
- Convert a notebook to a script.
- Test scripts in a terminal.
- Run a script as a command job.
- Use parameters in a command job.
Module 5: Learn how to track model training with MLflow in jobs when running scripts.
In this module, you learn how to:
- Use MLflow when you run a script as a job.
- Review metrics, parameters, artifacts, and models from a run.
Module 6: Azure Machine Learning Python SDK v2.
In this module, you'll learn how to:
- Log models with MLflow.
- Understand the MLmodel format.
- Register an MLflow model in Azure Machine Learning.
Module 7: Learn how to deploy models to a managed online endpoint for real-time inferencing.
In this module, you'll learn how to:
- Use managed online endpoints.
- Deploy your MLflow model to a managed online endpoint.
- Deploy a custom model to a managed online endpoint.
- Test online endpoints.

Syllabus

Module 1: Module 1: Make data available in Azure Machine Learning
- Introduction
- Understand URIs
- Create a datastore
- Create a data asset
- Exercise - Make data available in Azure Machine Learning
- Knowledge check
- Summary
Module 2: Module 2: Work with compute targets in Azure Machine Learning
- Introduction
- Choose the appropriate compute target
- Create and use a compute instance
- Create and use a compute cluster
- Exercise - Work with compute resources
- Knowledge check
- Summary
Module 3: Module 3: Work with environments in Azure Machine Learning
- Introduction
- Understand environments
- Explore and use curated environments
- Create and use custom environments
- Exercise - Work with environments
- Knowledge check
- Summary
Module 4: Module 4: Run a training script as a command job in Azure Machine Learning
- Introduction
- Convert a notebook to a script
- Run a script as a command job
- Use parameters in a command job
- Exercise - Run a training script as a command job
- Knowledge check
- Summary
Module 5: Module 5: Track model training with MLflow in jobs
- Introduction
- Track metrics with MLflow
- View metrics and evaluate models
- Exercise - Use MLflow to track training jobs
- Knowledge check
- Summary
Module 6: Module 6: Register an MLflow model in Azure Machine Learning
- Introduction
- Log models with MLflow
- Understand the MLflow model format
- Register an MLflow model
- Exercise - Log and register models with MLflow
- Knowledge check
- Summary
Module 7: Module 7: Deploy a model to a managed online endpoint
- Introduction
- Explore managed online endpoints
- Deploy your MLflow model to a managed online endpoint
- Deploy a model to a managed online endpoint
- Test managed online endpoints
- Exercise - Deploy an MLflow model to an online endpoint
- Knowledge check
- Summary