Book Image

Automated Machine Learning with Microsoft Azure

By : Dennis Michael Sawyers
Book Image

Automated Machine Learning with Microsoft Azure

By: Dennis Michael Sawyers

Overview of this book

Automated Machine Learning with Microsoft Azure will teach you how to build high-performing, accurate machine learning models in record time. It will equip you with the knowledge and skills to easily harness the power of artificial intelligence and increase the productivity and profitability of your business. Guided user interfaces (GUIs) enable both novices and seasoned data scientists to easily train and deploy machine learning solutions to production. Using a careful, step-by-step approach, this book will teach you how to use Azure AutoML with a GUI as well as the AzureML Python software development kit (SDK). First, you'll learn how to prepare data, train models, and register them to your Azure Machine Learning workspace. You'll then discover how to take those models and use them to create both automated batch solutions using machine learning pipelines and real-time scoring solutions using Azure Kubernetes Service (AKS). Finally, you will be able to use AutoML on your own data to not only train regression, classification, and forecasting models but also use them to solve a wide variety of business problems. By the end of this Azure book, you'll be able to show your business partners exactly how your ML models are making predictions through automatically generated charts and graphs, earning their trust and respect.
Table of Contents (17 chapters)
1
Section 1: AutoML Explained – Why, What, and How
5
Section 2: AutoML for Regression, Classification, and Forecasting – A Step-by-Step Guide
10
Section 3: AutoML in Production – Automating Real-Time and Batch Scoring Solutions

Explaining your AutoML model

Knowing your results is important, but knowing how your model derived its results is just as integral to working with machine learning. Here is where model explainability plays a key role. Explainability is the ability to say which features are most important in building your AutoML model. This is especially important in industries where you have to be able to legally explain your machine learning models, for example, if you built a model to determine who is approved for a loan:

  1. To begin, click the Explanations tab next to Metrics.
  2. Click the first ID under Explanation ID on the right-hand side of the screen.
  3. Click the slider button next to View previous dashboard experience.
  4. Click Global Importance.

    Immediately, you will see your columns ranked in order of importance. Sex is the most important column, followed by Pclass and Age, as shown in Figure 3.17. With an importance value of 1.1, Sex is roughly twice as important as Pclass, with...