Automated Machine Learning with Microsoft Azure by Dennis Michael Sawyers

Automated Machine Learning with Microsoft Azure by Dennis Michael Sawyers

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 DescriptionAutomated 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. 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 and discover how to quickly deliver value Create real-time scoring solutions with AutoML and Azure Kubernetes Service Train a large number of AutoML models at once using the AzureML Python SDK Who this book is forData scientists, aspiring data scientists, machine learning engineers, or anyone interested in applying artificial intelligence or machine learning in their business will find this machine learning 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. Familiarity with Python will help you implement the more advanced features found in the chapters, but even data analysts and SQL experts will be able to train ML models after finishing this book.

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.

SKU	Unavailable
ISBN 13	9781800565319
ISBN 10	1800565313
Title	Automated Machine Learning with Microsoft Azure
Author	Dennis Michael Sawyers
Condition	Unavailable
Binding Type	Paperback
Publisher	Packt Publishing Limited
Year published	2021-04-23
Number of pages	340
Cover note	Book picture is for illustrative purposes only, actual binding, cover or edition may vary.
Note	Unavailable