Learn about how to connect to data from the Azure Machine Learning workspace. You're introduced to datastores and data assets.
In this module, you learn how to:
- Access data by using Uniform Resource Identifiers (URIs).
- Connect to cloud data sources with datastores.
- Use data asset to access specific files or folders.
Learn how to work with compute targets in Azure Machine Learning. Compute targets allow you to run your machine learning workloads. Explore how and when you can use a compute instance or compute cluster.
In this module, you'll learn how to:
- Choose the appropriate compute target.
- Work with compute instances and clusters.
- Manage installed packages with environments.
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.
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.
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.
Learn how to register an Mlflow model in Azure Machine Learning.
In this module, you learn how to:
- Log models with MLflow.
- Understand the MLmodel format.
- Register an MLflow model in Azure Machine Learning.
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

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
- Module assessment
- Summary
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
- Module assessment
- Summary
Work with environments in Azure Machine Learning
- Introduction
- Understand environments
- Explore and use curated environments
- Create and use custom environments
- Exercise - Work with environments
- Module assessment
- Summary
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
- Module assessment
- Summary
Track model training with MLflow in jobs
- Introduction
- Track metrics with MLflow
- View metrics and evaluate models
- Exercise - Use MLflow to track training jobs
- Module assessment
- Summary
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
- Module assessment
- Summary
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
- Module assessment
- Summary