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Dino Esposito - Programming ML.NET (Developer Reference)

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With .NET 5s ML.NET and Programming ML.NET, any Microsoft .NET developer can solve serious machine learning problems, increasing their value and competitiveness in some of todays fastest-growing areas of software development. World-renowned Microsoft development expert Dino Esposito covers everything you need to know about ML.NET, the machine learning pipeline, and real-world machine learning solutions development.
Modeled on his popular Programming ASP.NET books, this guide takes the same scenario-based approach Microsofts team used to build the ML.NET framework itself. Esposito presents and illuminates ML.NETs dedicated mini-frameworks (ML Tasks) for specific classes of problems, and draws on personal experience to help developers apply these in the real world, where a problems complexity can vary widely based on data availability or the specific results you need. In a full section on ML.NET neural networks, Esposito introduces key concepts and presents realistic examples you can reuse in your own applications. Along the way, Esposito also shows how to leverage powerful Python-based machine learning tools in the .NET environment.
Programming ML.NET will help you add machine learning and artificial intelligence to your tool belt, whether you have a background in these high-demand technologies or not.

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Programming MLNet Dino Esposito Francesco Esposito Programming MLNet - photo 1
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Programming ML.Net

Dino Esposito
Francesco Esposito

Programming ML.Net

Published with the authorization of Microsoft Corporation by:

Pearson Education, Inc.

Copyright 2022 by Pearson Education, Inc.

All rights reserved. This publication is protected by copyright, and permission must be obtained from the publisher prior to any prohibited reproduction, storage in a retrieval system, or transmission in any form or by any means, electronic, mechanical, photocopying, recording, or likewise. For information regarding permissions, request forms, and the appropriate contacts within the Pearson Education Global Rights & Permissions Department, please visit www.pearson.com/permissions.

No patent liability is assumed with respect to the use of the information contained herein. Although every precaution has been taken in the preparation of this book, the publisher and author assume no responsibility for errors or omissions. Nor is any liability assumed for damages resulting from the use of the information contained herein.

ISBN-13: 978-0-13-738365-8

ISBN-10: 0-13-738365-7

Library of Congress Control Number: 2021952995

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Trademarks

Microsoft and the trademarks listed at http://www.microsoft.com on the Trademarks webpage are trademarks of the Microsoft group of companies. All other marks are property of their respective owners.

Warning and Disclaimer

Every effort has been made to make this book as complete and as accurate as possible, but no warranty or fitness is implied. The information provided is on an as is basis. The author, the publisher, and Microsoft Corporation shall have neither liability nor responsibility to any person or entity with respect to any loss or damages arising from the information contained in this book or from the use of the programs accompanying it.

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Editor-in-Chief

Brett Bartow

Executive Editor

Loretta Yates

Sponsoring Editor

Charvi Arora

Development Editor

Rick Kughen

Managing Editor

Sandra Schroeder

Senior Project Editor

Tracey Croom

Copy Editor

Rick Kughen

Indexer

Timothy Wright

Proofreader

Abigail Manheim

Technical Editor

Bri Achtman

Cover Designer

Twist Creative, Seattle

Compositor

codeMantra

To Silvia, Michela and new dreams

Dino Esposito

To my loved ones, to whom I couldnt help but dedicate a book

Francesco Esposito

Acknowledgments

FROM DINO:

Its the second time that the two of us, father and son, have written a machine learning book and a lot has changed since our last one, two years ago. In this book, we really joined forcesI put my software experience on the table, and Francesco gave his freshness, energy, and mathematical skills. We learned both how tricky it can be to put machine learning solutions in production and how easy it can be to hide those little gems in the folds of normal ASP.NET applications.

In the past two years, we achieved other results and, for example, we solidified our grasp of software for professional tennis and expanded to healthcare, agriculture, and customer care. The common denominator is always that one: intelligent software that ends up doing intelligent things. Its not about replacing humans and killing jobsquite the reverse. Its about replacing tasks with automated procedures, thus freeing humans from boring, automatable tasks and keeping them engaged in more interesting activities.

With Giorgio Garcia-Agreda and Gaetano Guarino, and the entire Crionet crew, were making our tennis fanatic dreams bigger every day. We are changing the games. With Vito Lanzotti and the KBMS Data Force team, were making silent history by turning doctors operating dreams into concrete and applicable artifacts, thus smoothing the way patients receive care. With Salvo Intilisano and Daniel Intilisano of Karma Enterprise, it was really a matter of technical karma. Same mindset, same vision, and same father-and-son business model! Agriculture wont be the same after the project ends, and the bees will be grateful!

The Youbiquitous team is growing, and the business is now spread over multiple pairs of strong shouldersmainly those of Matteo, Luciano, Martina, Filippo, and Gabriele. Thank you all for taking the time to keep the business running while we were having fun with ML.NET.

Finally, any book is teamwork, and it is our pleasure to call out the names of those who made it ultimately possible. Last but not certainly least! A monumental thank-you goes to Loretta Yates as the acquisition editor, Charvi Arora as the sponsoring editor, Rick Kughen as the development and copy editor, and Bri Achtman as the technical editor.

FROM FRANCESCO:

Im 23, grown enough to live alone but young enough to feel my heart beating for my grandparents. Sadly enough, the number is smaller than a book ago. A warm thought goes to Grandpa Salvatore and a hug to Grandma Concetta and Grandma Leda: I love you. On an even more personal side, this book is for Gianfrancofriend, business partner, second father, and grandfather. He taught me how to do things right and forgot to teach me how to do it wrong. And this book is also for Michela, who is strong enough to pursue her own wayno matter whatand smart enough to choose a good path!

Introduction

We need men who can dream of things that never were, and ask, why not?

John F. Kennedy, Speech to the Irish Parliament, June 1963

Today, the quest for data scientists is continuous, the data seems to be abundant, and cloud computing power is available. Is it the perfect world for the definitive triumph of machine learning? As we see things, we have all the necessary ingredients to cook up the applied AI, but we still lack a clear and effective method for combining them.

The purpose of data science is, like the purpose of science, to show that something is possible. Data science, though, doesnt productionize solutions. Thats the purpose of another branch of the machine learning universedata engineering.

Companies are wildly looking for data scientists, but the outcome of a good data science team is typically a runnable model whose software quality is often that of a prototype rather than of a production-ready artifact. Algorithms are tightly bound to data, and data must be complete, clean, and balanced. Whos in charge of this part of the job is often unclear, and as a result, the job is often partially done at best. Yet, a data science team disconnected from the rest of the applied AI pipeline that makes it to production is still a due investment for a large organization whose business produces large quantities of data (such as energy utilities, financial institutions, and manufacturing farms). For smaller companies with significantly more limited budgets, the outcome of some applied data science can be cheaper to buy as a service.

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