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Automated Machine Learning with Microsoft Azure

You're reading from Automated Machine Learning with Microsoft Azure

Product type Book

Published in Apr 2021

Publisher Packt

ISBN-13 9781800565319

Pages 340 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Author (1):

Dennis Michael Sawyers

Table of Contents (17) Chapters

Preface

Section 1: AutoML Explained – Why, What, and How

Chapter 1: Introducing AutoML

Chapter 2: Getting Started with Azure Machine Learning Service

Chapter 3: Training Your First AutoML Model

Section 2: AutoML for Regression, Classification, and Forecasting – A Step-by-Step Guide

Chapter 4: Building an AutoML Regression Solution

Chapter 5: Building an AutoML Classification Solution

Chapter 6: Building an AutoML Forecasting Solution

Chapter 7: Using the Many Models Solution Accelerator

Section 3: AutoML in Production – Automating Real-Time and Batch Scoring Solutions

Chapter 8: Choosing Real-Time versus Batch Scoring

Chapter 9: Implementing a Batch Scoring Solution

Chapter 10: Creating End-to-End AutoML Solutions

Chapter 11: Implementing a Real-Time Scoring Solution

Chapter 12: Realizing Business Value with AutoML

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Section 2: AutoML for Regression, Classification, and Forecasting – A Step-by-Step Guide

This second part focuses on training you to perform classification, regression, and forecasting machine learning (ML) jobs using Azure AutoML through the Azure ML Python SDK.

This section comprises the following chapters:

Chapter 4, Building an AutoML Regression Solution
Chapter 5, Building an AutoML Classification Solution
Chapter 6, Building an AutoML Forecasting Solution
Chapter 7, Using the Many Models Solution Accelerator

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Authors (1)

Dennis Michael Sawyers

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.

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