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)
Section 1: AutoML Explained – Why, What, and How
Section 2: AutoML for Regression, Classification, and Forecasting – A Step-by-Step Guide
Section 3: AutoML in Production – Automating Real-Time and Batch Scoring Solutions

Architecting batch scoring solutions

Batch inferencing refers to scoring new data points in batches on a recurring time-based schedule. New data is collected over time and subsequently scored, generating new predictions. This is the most common way modern companies use ML models.

In this section, you will learn how to architect a complete, end-to-end batch scoring solution using Azure AutoML-trained models. You will also learn why, and in what situations, you should prioritize batch scoring over real-time scoring solutions.

Understanding the five-step batch scoring process

Each batch scoring solution you make should follow a five-step process. This process begins by training and registering an ML model as you did in the previous chapters using AMLS. Regression, classification, and forecasting models all follow the same pattern. In order, the five steps are as follows:

  1. Train a model. You can train a model either using the AMLS GUI as you did in Chapter 3, Training...