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

Summary

AI and machine learning may have captured the world's imagination, but there's a large gap between the pie-in-the-sky promises of AI and the reality of AI projects. Machine learning projects, in particular, fail often and slowly. Traditional managers treat data science projects like software engineering projects, and data scientists work in a manual, time-consuming manner. Luckily, AutoML has emerged as a way to speed up projects, and Microsoft has created its AutoML offering with your needs in mind.

You are now primed for Chapter 2, Getting Started with Azure Machine Learning Service, which will introduce you to the Microsoft Azure Machine Learning workspace. You will create an Azure Machine Learning workspace and all of the necessary components required to start an AutoML project. By the end of the chapter, you will have a firm grasp of all of the different components of Azure Machine Learning Studio and how they interact.