Automated Machine Learning with Microsoft Azure: Build highly accurate and scalable end-to-end AI solutions with Azure AutoML

A practical, step-by-step guide to using Microsoft's AutoML technology on the Azure Machine Learning service for developers and data scientists working with the Python programming language

Key Features

• Create, deploy, productionalize, and scale automated machine learning solutions on Microsoft Azure
• Improve the accuracy of your ML models through automatic data featurization and model training
• Increase productivity in your organization by using artificial intelligence to solve common problems

Book Description

Automated Machine Learning with Microsoft Azure helps you to build high-performing, accurate machine learning models in record time. It allows anyone to easily harness the power of artificial intelligence and increase the productivity and profitability of your business. With a series of clicks on a guided user interface (GUI), novices and seasoned data scientists alike can train and deploy machine learning solutions to production with ease.

This book will teach you how to use Azure AutoML with both the GUI as well as the AzureML Python software development kit (SDK) in a careful, step-by-step way. 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.

What you will learn

• Understand how to train classification, regression, and forecasting ML algorithms with Azure AutoML
• Prepare data for Azure AutoML to ensure smooth model training and deployment
• Adjust AutoML configuration settings to make your models as accurate as possible
• Determine when to use a batch-scoring solution versus a real-time scoring solution
• Productionalize your AutoML solution with Azure Machine Learning pipelines
• Create real-time scoring solutions with AutoML and Azure Kubernetes Service
• Discover how to quickly deliver value and earn business trust using AutoML
• Train a large number of AutoML models at once using the AzureML Python SDK

Who this book is for

Data scientists, aspiring data scientists, machine learning engineers, or anyone interested in applying artificial intelligence or machine learning in their business will find this book useful. You need to have beginner-level knowledge of artificial intelligence and a technical background in computer science, statistics, or information technology before getting started with this machine learning book. Familiarity with Python will help you implement this book's more advanced features, but even data analysts and SQL experts will be able to train ML models after finishing this book.

Table of Contents

1. Introducing AutoML
2. Getting Started with Azure Machine Learning Service
3. Training Your First AutoML Model
4. Building an AutoML Regression Solution
5. Building an AutoML Classification Solution
6. Building an AutoML Forecasting Solution
7. Using the Many Models Solution Accelerator
8. Choosing Real-Time versus Batch Scoring
9. Implementing a Batch Scoring Solution
10. Creating End-to-End AutoML Solutions
11. Implementing a Real-Time Scoring Solution
12. Realizing Business Value with AutoML

Dennis Michael Sawyers is a senior cloud solutions architect (CSA) at Microsoft, specializing in data and AI. In his role as a CSA, he helps Fortune 500 companies leverage Microsoft Azure cloud technology to build top-class machine learning and AI solutions. Prior to his role at Microsoft, he was a data scientist at Ford Motor Company in Global Data Insight and Analytics (GDIA) and a researcher in anomaly detection at the highly regarded Carnegie Mellon Auton Lab. He received a master's degree in data analytics from Carnegie Mellon's Heinz College and a bachelor's degree from the University of Michigan. More than anything, Dennis is passionate about democratizing AI solutions through automated machine learning technology.