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- As a data scientist, you can use Azure Machine Learning to train and manage your machine learning models. Learn what Azure Machine Learning is, and get familiar with all its resources and assets.
In this module, you learn how to:
- Create an Azure Machine Learning workspace.
- Identify resources and assets.
- Train models in the workspace.
- Learn how you can interact with the Azure Machine Learning workspace. You can use the Azure Machine Learning studio, the Python SDK (v2), or the Azure CLI (v2).
In this module, you'll learn how and when to use:
- The Azure Machine Learning studio.
- The Python Software Development Kit (SDK).
- The Azure Command Line Interface (CLI).
- 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.
