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 AutoML results to your business

To realize business value, your AutoML models must be implemented and used by the business. A common obstacle to implementation is a lack of trust stemming from a lack of understanding of how ML works. At the same time, explaining the ins and outs of how individual ML algorithms work is a poor way to gain trust. Throwing math symbols and complicated statistics at end users will not work unless they already have a deep background in mathematics.

Instead, use AutoML's inbuilt explainability. As long as you enable explainability when training models, you can say exactly which features AutoML is using to generate predictions. In general, it's a good practice to do the following four things:

  • Always enable explainability when training any AutoML model.
  • When presenting results to the business, first show performance, then show explainability.
  • Rank the features in order of most to least important.
  • Drop any unimportant...