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Barga Roger - Predictive Analytics with Microsoft Azure Machine Learning : Build and Deploy Actionable Solutions in Minutes

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Barga Roger Predictive Analytics with Microsoft Azure Machine Learning : Build and Deploy Actionable Solutions in Minutes

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Data Science and Machine Learning are in high demand, as customers are increasingly looking for ways to glean insights from all their data. More customers now realize that Business Intelligence is not enough as the volume, speed and complexity of data now defy traditional analytics tools. While Business Intelligence addresses descriptive and diagnostic analysis, Data Science unlocks new opportunities through predictive and prescriptive analysis.

The purpose of this book is to provide a gentle and instructionally organized introduction to the field of data science and machine learning, with a focus on building and deploying predictive models.

The book also provides a thorough overview of the Microsoft Azure Machine Learning service using task oriented descriptions and concrete end-to-end examples, sufficient to ensure the reader can immediately begin using this important new service. It describes all aspects of the service from data ingress to applying machine learning and evaluating the resulting model, to deploying the resulting model as a machine learning web service. Finally, this book attempts to have minimal dependencies, so that you can fairly easily pick and choose chapters to read. When dependencies do exist, they are listed at the start and end of the chapter.

The simplicity of this new service from Microsoft will help to take Data Science and Machine Learning to a much broader audience than existing products in this space. Learn how you can quickly build and deploy sophisticated predictive models as machine learning web services with the new Azure Machine Learning service from Microsoft.

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Predictive Analytics with Microsoft Azure Machine Learning

Build and Deploy Actionable Solutions in Minutes

Predictive Analytics with Microsoft Azure Machine Learning Build and Deploy Actionable Solutions in Minutes - image 1

Roger Barga

Valentine Fontama

Wee Hyong Tok

Predictive Analytics with Microsoft Azure Machine Learning Build and Deploy Actionable Solutions in Minutes - image 2

Predictive Analytics with Microsoft Azure Machine Learning: Build and Deploy Actionable Solutions in Minutes

Copyright 2014 by Roger Barga, Valentine Fontama, and Wee Hyong Tok

This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publishers location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law.

ISBN-13 (pbk): 978-1-4842-0446-7

ISBN-13 (electronic): 978-1-4842-0445-0

Trademarked names, logos, and images may appear in this book. Rather than use a trademark symbol with every occurrence of a trademarked name, logo, or image we use the names, logos, and images only in an editorial fashion and to the benefit of the trademark owner, with no intention of infringement of the trademark.

The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights.

While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.

Managing Director: Welmoed Spahr

Lead Editor: James DeWolf

Development Editor: Douglas Pundick

Technical Reviewers: Jacob Spoelstra and Hang Zhang

Editorial Board: Steve Anglin, Mark Beckner, Gary Cornell, Louise Corrigan, James DeWolf, Jonathan Gennick, Robert Hutchinson, Michelle Lowman, James Markham, Matthew Moodie, Jeff Olson, Jeffrey Pepper, Douglas Pundick, Ben Renow-Clarke, Dominic Shakeshaft, Gwenan Spearing, Matt Wade, Steve Weiss

Coordinating Editor: Kevin Walter

Copy Editor: Mary Behr

Compositor: SPi Global

Indexer: SPi Global

Artist: SPi Global

Cover Designer: Anna Ishchenko

Distributed to the book trade worldwide by Springer Science+Business Media New York, 233 Spring Street, 6th Floor, New York, NY 10013. Phone 1-800-SPRINGER, fax (201) 348-4505, e-mail . Apress Media, LLC is a California LLC and the sole member (owner) is Springer Science + Business Media Finance Inc (SSBM Finance Inc). SSBM Finance Inc is a Delaware corporation.

For information on translations, please e-mail .

Apress and friends of ED books may be purchased in bulk for academic, corporate, or promotional use. eBook versions and licenses are also available for most titles. For more information, reference our Special Bulk SaleseBook Licensing web page at www.apress.com/bulk-sales .

Any source code or other supplementary materials referenced by the author in this text is available to readers at www.apress.com . For detailed information about how to locate your books source code, go to www.apress.com/source-code/ .

Contents at a Glance

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About the Authors Roger Barga is a General Manager and Director of Developm - photo 22

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About the Authors Roger Barga is a General Manager and Director of - photo 24

About the Authors

Roger Barga is a General Manager and Director of Development at Amazon Web - photo 25

Roger Barga is a General Manager and Director of Development at Amazon Web Services. Prior to joining Amazon, Roger was Group Program Manager for the Cloud Machine Learning group in the Cloud & Enterprise division at Microsoft, where his team was responsible for product management of the Azure Machine Learning service. Roger joined Microsoft in 1997 as a Researcher in the Database Group of Microsoft Research, where he directed both systems research and product development efforts in database, workflow, and stream processing systems. He has developed ideas from basic research, through proof of concept prototypes, to incubation efforts in product groups. Prior to joining Microsoft, Roger was a Research Scientist in the Machine Learning Group at the Pacific Northwest National Laboratory where he built and deployed machine learning-based solutions. Roger is also an Affiliate Professor at the University of Washington, where he is a lecturer in the Data Science and Machine Learning programs.

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