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This repository contains training material related to Azure and Machine Learning


Welcome to the ACE-team training on Azure Machine Learning (AML) service.

The material presented here is a deep-dive which combine real-world data science scenarios with many different technologies including Azure Databricks (ADB), Azure Machine Learning (AML) Services and Azure DevOps, with the goal of creating, deploying, and maintaining end-to-end data science and AI solutions.

Anomaly Detection in structured data

Note: Anomaly detection can also be performed on unstructured data. One example is to detect unusual behavior in videos, like a car driving on a sidewalk, or violation of safety protocols on a manufacturing floor. If you are interested in this use case, please go to this repo:


Please go to this page to find alternative agendas around the above use-cases.




You will need this basic knowledge:

  1. Basic data science and machine learning concepts.
  2. Moderate skills in coding with Python and machine learning using Python.
  3. Familiarity with Jupyter Notebooks and/or Databricks Notebooks.
  4. Familiarity with Azure databricks.
  5. Basic skills using Git version control.

If you do not have any of the above pre-requisites, please find below links:

  1. To Watch: Data Science for Beginners
  2. To Watch: Get Started with Azure Machine Learning
  3. To Watch: Python for Data Science: Introduction
  4. To Watch: Jupyter Notebook Tutorial: Introduction, Setup, and Walkthrough
  5. To Do: Go to [] and create and run a Jupyter notebook with Python
  6. To Watch: Azure Databricks: A brief introduction
  7. To Read (10 mins): Git Handbook


  1. An Azure Subscription (unless provided to you).
  2. If you are not provided with a managed lab environment (course invitation will specify), then follow these instructions for configuring your development environment prior to the course or if you do it on your own. You will need an Azure Subscription (unless one is provided to you). Pay particular attention to version numbers, such as the version of the Spark runtime.


We invite everybody to contribute.