July 18, 2019

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Python notebooks with ML and deep learning examples with Azure Machine Learning | Microsoft

repo name Azure/MachineLearningNotebooks
repo link https://github.com/Azure/MachineLearningNotebooks
homepage https://docs.microsoft.com/azure/machine-learning/service/
language Jupyter Notebook
size (curr.) 59925 kB
stars (curr.) 1397
created 2018-08-17
license MIT License

Azure Machine Learning service example notebooks

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.

Azure ML Workflow

Quick installation

pip install azureml-sdk

Read more detailed instructions on how to set up your environment using Azure Notebook service, your own Jupyter notebook server, or Docker.

How to navigate and use the example notebooks?

If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, 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. This index should assist in navigating the Azure Machine Learning notebook samples and encourage efficient retrieval of topics and content.

If you want to…


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

How to use Azure ML

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
  • Monitor Models - Examples showing how to enable model monitoring services such as DataDrift


Community Repository

Visit this community repository to find useful end-to-end sample notebooks. Also, please follow these contribution guidelines when contributing to this repository.

Projects using Azure Machine Learning

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


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To opt out of tracking, please go to the raw markdown or .ipynb files and remove the following line of code:


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