Machine Learning on Azure

© Elephant Scale

Jan. 03, 2020


Machine Learning (ML) is a game-changer and Microsoft Azure is the second-largest cloud computing provider. Azure’s particular strength is in the usability and integration with the Microsoft stack.

In this course, the students will get an overview of ML with Python and R, the two standard environments for ML. They will also learn the specifics of the Azure and the capabilities that it offers to ML developers.

One of the goals of the course is to prepare the students for taking the Azure certification exam.

What you will learn

  • Get introduced to Machine Learning
  • Find out how to use Machine Learning tools on Microsoft Azure
  • Understand how to use automated Machine Learning
  • Learn the best practices and real-world use cases


Developers, Architects


Four days


Lectures and hands-on labs. (50%, 50%)


  • Interest in Machine Learning (Machine Learning overview is included in the course)
  • Familiarity with Python or R is a plus

Lab environment

  • A reasonably modern laptop
  • Unrestricted connection to the Internet. Laptops with overly restrictive VPNs or firewalls may not work properly
  • Chrome browser
    • SSH client for your platform

Detailed outline

Machine Learning Overview

    • Model Parameters
    • Hyperparameters
    • Understand the Decision Process
    • Establish Performance Metrics
    • Focus on Transparency to Gain Trust
    • Embrace Experimentation
    • Don’t Operate in a Silo
    • An Iterative and Time-Consuming Process
    • Feature Engineering
    • Algorithm Selection
    • Growing Demand

How Automated Machine Learning Works

    • What Is Automated Machine Learning?
    • Understanding Data
    • Detecting Tasks
    • Choosing Evaluation Metrics
    • Monitoring and Retraining
    • Automated ML
    • Guardrails
    • End-to-End Model Life-Cycle Management

Microsoft Azure Machine Learning and Automated ML

    • The Machine Learning Process
    • Collaboration and Monitoring
    • Deployment
    • Setting Up an Azure Machine Learning Workspace for Automated ML
    • Azure Notebooks
    • Notebook VM

Feature Engineering and Automated Machine Learning

    • Auto-Featurization
    • Deploying Automated Machine Learning Models

Azure Databricks – Spark

    • Databricks Environment
    • Machine Learning on Databricks
    • Linear Regression
    • Logistic Regression
    • SVN
    • Decision Trees, Random Forests
    • Using Automated ML for Classification and Regression

How Enterprises Are Using Automated Machine Learning

    • Model Interpretability and Transparency with Automated ML
    • Guardrails

Automated ML for Everyone

    • Azure Portal UI
    • Power BI
    • Preparing the Data
    • Automated ML Training
    • Understanding the Best Model
    • Understanding the Automated ML Training Process
    • Model Deployment and Inferencing
    • Enabling Collaboration