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 real-time scoring solutions

Real-time inferencing refers to scoring new data points as they arrive instead of on a time-based schedule. New data flows in, new predictions come out. While not as common as batch inferencing, real-time inferencing is used by companies in a number of scenarios such as credit card fraud detection, anomaly detection on the factory floor, and recommending products when you're online shopping.

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

Understanding the four-step real-time scoring process

Real-time scoring solutions follow a slightly different process than batch scoring solutions. There are only four steps. Like batch solutions, the process begins by training an ML model and registering it as you did in previous chapters....