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 AutoML solutions

Architecting AutoML solutions refers to drawing end-to-end diagrams. These act as blueprints for how you should build out your solution, and also can be used to explain to your end users how everything works. While many IT solutions are complex and can take many forms, AutoML-based solutions follow standard patterns that require you to make a few important decisions.

In this section, you'll first learn what decisions to make before architecting a decision. Then, you will learn how to architect an end-to-end batch scoring solution and an end-to-end real-time scoring solution that's easy to explain to end users. Although the architecture may be simplified, the more standard it is, the easier it is to implement, explain, and understand.

Making key architectural decisions for AutoML solutions

When drawing an architectural diagram, there are several key considerations you need to make, the most important being whether you need to make a batch...