This repository contains example notebooks demonstrating the Azure Machine Learning Python SDK which allows you to build, train, deploy and manage machine learning solutions using Azure. The AML SDK allows you the choice of using local or cloud compute resources, while managing and maintaining the complete data science workflow from the cloud.

Read more detailed instructions on how to set up your environment.

You should always run the Configuration notebook first when setting up a notebook library on a new machine or in a new environment. It configures your notebook library to connect to an Azure Machine Learning workspace, and sets up your workspace and compute to be used by many of the other examples.

If you want to...

The Tutorials folder contains notebooks for the tutorials described in the Azure Machine Learning documentation

The How to use Azure ML folder contains specific examples demonstrating the features of the Azure Machine Learning SDK

  • Training - Examples of how to build models using Azure ML's logging and execution capabilities on local and remote compute targets
  • Training with Deep Learning - Examples demonstrating how to build deep learning models using estimators and parameter sweeps
  • Manage Azure ML Service - Examples how to perform tasks, such as authenticate against Azure ML service in different ways.
  • Automated Machine Learning - Examples using Automated Machine Learning to automatically generate optimal machine learning pipelines and models
  • Machine Learning Pipelines - Examples showing how to create and use reusable pipelines for training and batch scoring
  • Deployment - Examples showing how to deploy and manage machine learning models and solutions
  • Azure Databricks - Examples showing how to use Azure ML with Azure Databricks

Visit following repos to see projects contributed by Azure ML users:

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