Manning.Machine.Learning.Systems.2018.5.pdf

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Machine Learning Systems

brief contents

contents

foreword

preface

acknowledgments

about this book

How this book is organized

Code conventions and downloads

Book forum

Other online resources

about the author

about the cover illustration

Part 1: Fundamentals of reactive machine learning

Chapter 1: Learning reactive machine learning

1.1 An example machine learning system

1.1.1 Building a prototype system

1.1.2 Building a better system

1.2 Reactive machine learning

1.2.1 Machine learning

1.2.2 Reactive systems

1.2.3 Making machine learning systems reactive

1.2.4 When not to use reactive machine learning

Chapter 2: Using reactive tools

2.1 Scala, a reactive language

2.1.1 Reacting to uncertainty in Scala

2.1.2 The uncertainty of time

2.2 Akka, a reactive toolkit

2.2.1 The actor model

2.2.2 Ensuring resilience with Akka

2.3 Spark, a reactive big data framework

Part 2: Building a reactive machine learning system

Chapter 3: Collecting data

3.1 Sensing uncertain data

3.2 Collecting data at scale

3.2.1 Maintaining state in a distributed system

3.2.2 Understanding data collection

3.3 Persisting data

3.3.1 Elastic and resilient databases

3.3.2 Fact databases

3.3.3 Querying persisted facts

3.3.4 Understanding distributed-fact databases

3.4 Applications

3.5 Reactivities

Chapter 4: Generating features

4.1 Spark ML

4.2 Extracting features

4.3 Transforming features

4.3.1 Common feature transforms

4.3.2 Transforming concepts

4.4 Selecting features

4.5 Structuring feature code

4.5.1 Feature generators

4.5.2 Feature set composition

4.6 Applications

4.7 Reactivities

Chapter 5: Learning models

5.1 Implementing learning algorithms

5.1.1 Bayesian modeling

5.1.2 Implementing Naive Bayes

5.2 Using MLlib

5.2.1 Building an ML pipeline

5.2.2 Evolving modeling techniques

5.3 Building facades

5.3.1 Learning artistic style

5.4 Reactivities

Chapter 6: Evaluating models

6.1 Detecting fraud

6.2 Holding out data

6.3 Model metrics

6.4 Testing models

6.5 Data leakage

6.6 Recording provenance

6.7 Reactivities

Chapter 7: Publishing models

7.1 The uncertainty of farming

7.2 Persisting models

7.3 Serving models

7.3.1 Microservices

7.3.2 Akka HTTP

7.4 Containerizing applications

7.5 Reactivities

Chapter 8: Responding

8.1 Moving at the speed of turtles

8.2 Building services with tasks

8.3 Predicting traffic

8.4 Handling failure

8.5 Architecting response systems

8.6 Reactivities

Part 3: Operating a machine learning system

Chapter 9: Delivering

9.1 Shipping fruit

9.2 Building and packaging

9.3 Build pipelines

9.4 Evaluating models

9.5 Deploying

9.6 Reactivities

Chapter 10: Evolving intelligence

10.1 Chatting

10.2 Artificial intelligence

10.3 Reflex agents

10.4 Intelligent agents

10.5 Learning agents

10.6 Reactive learning agents

10.6.1 Reactive principles

10.6.2 Reactive strategies

10.6.3 Reactive machine learning

10.7 Reactivities

10.7.1 Libraries

10.7.2 System data

10.8 Reactive explorations

10.8.1 Users

10.8.2 System dimensions

10.8.3 Applying reactive principles

Appendix: Getting set up

Scala

Git code repository

sbt

Spark

Couchbase

Docker

index

Symbols

M A N N I N G Designs that scale Jeff Smith Foreword by Sean Owen

Machine Learning Systems DESIGNS THAT SCALE JEFF SMITH FOREWORD BY SEAN OWEN M A N N I N G SHELTER ISLAND

For online information and ordering of this and other Manning books, please visit www.manning.com. The publisher offers discounts on this book when ordered in quantity. For more information, please contact Special Sales Department Manning Publications Co. 20 Baldwin Road PO Box 761 Shelter Island, NY 11964 Email: orders@manning.com ©2018 by Manning Publications Co. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by means electronic, mechanical, photocopying, or otherwise, without prior written permission of the publisher. Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in the book, and Manning Publications was aware of a trademark claim, the designations have been printed in initial caps or all caps. Recognizing the importance of preserving what has been written, it is Manning’s policy to have the books we publish printed on acid-free paper, and we exert our best efforts to that end. Recognizing also our responsibility to conserve the resources of our planet, Manning books are printed on paper that is at least 15 percent recycled and processed without the use of elemental chlorine. Manning Publications Co. 20 Baldwin Road PO Box 761 Shelter Island, NY 11964 Development editor: Susanna Kline Review editor: Aleksandar Dragosavljevic´ Technical development editor: Kostas Passadis Project editor: Tiffany Taylor Copyeditor: Corbin Collins Proofreader: Katie Tennant Technical proofreader: Jerry Kuch Typesetter: Gordan Salinovic Cover designer: Marija Tudor ISBN 9781617293337 Printed in the United States of America 1 2 3 4 5 6 7 8 9 10 – EBM – 23 22 21 20 19 18

brief contents PART 1 FUNDAMENTALS OF REACTIVE MACHINE LEARNING...................1 1 ■ Learning reactive machine learning 3 2 ■ Using reactive tools 23 PART 2 BUILDING A REACTIVE MACHINE LEARNING SYSTEM................41 3 ■ Collecting data 43 4 ■ Generating features 69 5 ■ Learning models 93 6 ■ Evaluating models 117 7 ■ Publishing models 135 8 ■ Responding 149 PART 3 OPERATING A MACHINE LEARNING SYSTEM.........................165 9 ■ Delivering 167 10 ■ Evolving intelligence 177 iii

contents foreword ix preface xii acknowledgments about this book about the author about the cover illustration xxii xv xvii xxi PART 1 FUNDAMENTALS OF REACTIVE MACHINE LEARNING .........1 1 Learning reactive machine learning 3 1.1 An example machine learning system 4 Building a prototype system 4 ■ Building a better system 7 1.2 Reactive machine learning 7 Machine learning 8 ■ Reactive systems 11 ■ Making machine- learning systems reactive 16 ■ When not to use reactive machine learning 20 2 Using reactive tools 2.1 23 Scala, a reactive language 24 Reacting to uncertainty in Scala 25 ■ The uncertainty of time 26 v

vi CONTENTS 2.2 Akka, a reactive toolkit 29 The actor model 29 ■ Ensuring resilience with Akka 31 2.3 Spark, a reactive big data framework 34 PART 2 BUILDING A REACTIVE MACHINE LEARNING SYSTEM ......41 3 Collecting data 43 3.1 Sensing uncertain data 44 3.2 Collecting data at scale 48 Maintaining state in a distributed system 48 ■ Understanding data collection 52 3.3 Persisting data 53 Elastic and resilient databases 54 ■ Fact databases 55 ■ Querying persisted facts 57 ■ Understanding distributed-fact databases 62 3.4 Applications 66 3.5 Reactivities 67 4 Generating features 69 Spark ML 71 4.1 4.2 Extracting features 71 4.3 Transforming features 74 Common feature transforms 76 ■ Transforming concepts 79 4.4 4.5 Selecting features 80 Structuring feature code 82 Feature generators 82 ■ Feature set composition 86 4.6 Applications 90 4.7 Reactivities 90 5 Learning models 5.1 93 Implementing learning algorithms 94 Bayesian modeling 96 ■ Implementing Naive Bayes 98 5.2 Using MLlib 102 Building an ML pipeline 102 ■ Evolving modeling techniques 107 5.3 Building facades 109 Learning artistic style 110 5.4 Reactivities 115

CONTENTS vii 6 Evaluating models 117 6.1 Detecting fraud 118 6.2 Holding out data 119 6.3 Model metrics 122 6.4 Testing models 127 6.5 Data leakage 129 6.6 Recording provenance 130 6.7 Reactivities 132 7 Publishing models 135 7.1 The uncertainty of farming 136 7.2 Persisting models 136 7.3 Serving models 141 Microservices 141 ■ Akka HTTP 142 7.4 Containerizing applications 144 7.5 Reactivities 147 8 Responding 149 8.1 Moving at the speed of turtles 150 8.2 Building services with tasks 150 8.3 Predicting traffic 153 8.4 Handling failure 157 8.5 Architecting response systems 160 8.6 Reactivities 162 PART 3 OPERATING A MACHINE LEARNING SYSTEM ...............165 9 Delivering 167 Shipping fruit 168 9.1 9.2 Building and packaging 169 9.3 Build pipelines 170 9.4 Evaluating models 171 9.5 Deploying 172 9.6 Reactivities 175

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