**Summary**
__Machine Learning Systems: Designs that scale__ is an example-rich guide that teaches you how to implement reactive design solutions in your machine learning systems to make them as reliable as a well-built web app.
Foreword by Sean Owen, Director of Data Science, Cloudera
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
**About the Technology**
If you're building machine learning models to be used on a small scale, you don't need this book. But if you're a developer building a production-grade ML application that needs quick response times, reliability, and good user experience, this is the book for you. It collects principles and practices of machine learning systems that are dramatically easier to run and maintain, and that are reliably better for users.
**About the Book**
__Machine Learning Systems: Designs that scale__ teaches you to design and implement production-ready ML systems. You'll learn the principles of reactive design as you build pipelines with Spark, create highly scalable services with Akka, and use powerful machine learning libraries like MLib on massive datasets. The examples use the Scala language, but the same ideas and tools work in Java, as well.
**What's Inside**
* Working with Spark, MLlib, and Akka
* Reactive design patterns
* Monitoring and maintaining a large-scale system
* Futures, actors, and supervision
**About the Reader**
Readers need intermediate skills in Java or Scala. No prior machine learning experience is assumed.
**About the Author**
**Jeff Smith** builds powerful machine learning systems. For the past decade, he has been working on building data science applications, teams, and companies as part of various teams in New York, San Francisco, and Hong Kong. He blogs (https://medium.com/@jeffksmithjr), tweets (@jeffksmithjr), and speaks (www.jeffsmith.tech/speaking) about various aspects of building real-world machine learning systems.
**Table of Contents**
PART 1 - FUNDAMENTALS OF REACTIVE MACHINE LEARNING1. Learning reactive machine learning
2. Using reactive tools
PART 2 - BUILDING A REACTIVE MACHINE LEARNING SYSTEM1. Collecting data
2. Generating features
3. Learning models
4. Evaluating models
5. Publishing models
6. Responding
PART 3 - OPERATING A MACHINE LEARNING SYSTEM1. Delivering
2. Evolving intelligence

nexusstc/Machine Learning Systems: Designs That Scale/36a63846daf74c89ce97fcb7632cf690.pdf

lgli/22443.pdf

lgrsnf/22443.pdf

zlib/Computers/Computer Science/Jeff Smith/Machine Learning Systems: Designs That Scale_3704494.pdf

Smith, Jeff

Manning Publications Co. LLC

Simon & Schuster, Shelter Island, NY, 2018

United States, United States of America

1st, First Edition, 2018

Jul 08, 2018

0

lg2337871

{"isbns":["1617293334","9781617293337"],"last_page":224,"publisher":"Manning Publications"}

Machine Learning Systems......Page 1
brief contents......Page 4
contents......Page 6
foreword......Page 10
preface......Page 13
acknowledgments......Page 16
about this book......Page 18
How this book is organized......Page 19
Book forum......Page 20
Other online resources......Page 21
about the author......Page 22
about the cover illustration......Page 23
Part 1: Fundamentals of reactive machine learning......Page 24
Chapter 1: Learning reactive machine learning......Page 26
1.1.1 Building a prototype system......Page 27
1.2 Reactive machine learning......Page 30
1.2.1 Machine learning......Page 31
1.2.2 Reactive systems......Page 34
1.2.3 Making machine learning systems reactive......Page 38
1.2.4 When not to use reactive machine learning......Page 43
Chapter 2: Using reactive tools......Page 46
2.1 Scala, a reactive language......Page 47
2.1.1 Reacting to uncertainty in Scala......Page 48
2.1.2 The uncertainty of time......Page 49
2.2.1 The actor model......Page 52
2.2.2 Ensuring resilience with Akka......Page 54
2.3 Spark, a reactive big data framework......Page 57
Part 2: Building a reactive machine learning system......Page 64
Chapter 3: Collecting data......Page 66
3.1 Sensing uncertain data......Page 67
3.2.1 Maintaining state in a distributed system......Page 71
3.2.2 Understanding data collection......Page 75
3.3 Persisting data......Page 76
3.3.1 Elastic and resilient databases......Page 77
3.3.2 Fact databases......Page 78
3.3.3 Querying persisted facts......Page 80
3.3.4 Understanding distributed-fact databases......Page 85
3.4 Applications......Page 89
3.5 Reactivities......Page 90
Chapter 4: Generating features......Page 92
4.2 Extracting features......Page 94
4.3 Transforming features......Page 97
4.3.1 Common feature transforms......Page 99
4.3.2 Transforming concepts......Page 102
4.4 Selecting features......Page 103
4.5.1 Feature generators......Page 105
4.5.2 Feature set composition......Page 109
4.7 Reactivities......Page 113
Chapter 5: Learning models......Page 116
5.1 Implementing learning algorithms......Page 117
5.1.1 Bayesian modeling......Page 119
5.1.2 Implementing Naive Bayes......Page 121
5.2.1 Building an ML pipeline......Page 125
5.2.2 Evolving modeling techniques......Page 130
5.3 Building facades......Page 132
5.3.1 Learning artistic style......Page 133
5.4 Reactivities......Page 138
Chapter 6: Evaluating models......Page 140
6.1 Detecting fraud......Page 141
6.2 Holding out data......Page 142
6.3 Model metrics......Page 145
6.4 Testing models......Page 150
6.5 Data leakage......Page 152
6.6 Recording provenance......Page 153
6.7 Reactivities......Page 155
Chapter 7: Publishing models......Page 158
7.2 Persisting models......Page 159
7.3.1 Microservices......Page 164
7.3.2 Akka HTTP......Page 165
7.4 Containerizing applications......Page 167
7.5 Reactivities......Page 170
Chapter 8: Responding......Page 172
8.2 Building services with tasks......Page 173
8.3 Predicting traffic......Page 176
8.4 Handling failure......Page 180
8.5 Architecting response systems......Page 183
8.6 Reactivities......Page 185
Part 3: Operating a machine learning system......Page 188
Chapter 9: Delivering......Page 190
9.1 Shipping fruit......Page 191
9.2 Building and packaging......Page 192
9.3 Build pipelines......Page 193
9.4 Evaluating models......Page 194
9.5 Deploying......Page 195
9.6 Reactivities......Page 198
10.1 Chatting......Page 200
10.3 Reflex agents......Page 201
10.4 Intelligent agents......Page 203
10.5 Learning agents......Page 204
10.6.1 Reactive principles......Page 208
10.7 Reactivities......Page 209
10.7.1 Libraries......Page 210
10.7.2 System data......Page 211
10.8.1 Users......Page 213
10.8.2 System dimensions......Page 214
10.8.3 Applying reactive principles......Page 215
sbt......Page 218
Docker......Page 219
D......Page 220
K......Page 221
Q......Page 222
Z......Page 223

Machine Learning Systems 1
brief contents 4
contents 6
foreword 10
preface 13
acknowledgments 16
about this book 18
How this book is organized 19
Code conventions and downloads 20
Book forum 20
Other online resources 21
about the author 22
about the cover illustration 23
Part 1: Fundamentals of reactive machine learning 24
Chapter 1: Learning reactive machine learning 26
1.1 An example machine learning system 27
1.1.1 Building a prototype system 27
1.1.2 Building a better system 30
1.2 Reactive machine learning 30
1.2.1 Machine learning 31
1.2.2 Reactive systems 34
1.2.3 Making machine learning systems reactive 38
1.2.4 When not to use reactive machine learning 43
Chapter 2: Using reactive tools 46
2.1 Scala, a reactive language 47
2.1.1 Reacting to uncertainty in Scala 48
2.1.2 The uncertainty of time 49
2.2 Akka, a reactive toolkit 52
2.2.1 The actor model 52
2.2.2 Ensuring resilience with Akka 54
2.3 Spark, a reactive big data framework 57
Part 2: Building a reactive machine learning system 64
Chapter 3: Collecting data 66
3.1 Sensing uncertain data 67
3.2 Collecting data at scale 71
3.2.1 Maintaining state in a distributed system 71
3.2.2 Understanding data collection 75
3.3 Persisting data 76
3.3.1 Elastic and resilient databases 77
3.3.2 Fact databases 78
3.3.3 Querying persisted facts 80
3.3.4 Understanding distributed-fact databases 85
3.4 Applications 89
3.5 Reactivities 90
Chapter 4: Generating features 92
4.1 Spark ML 94
4.2 Extracting features 94
4.3 Transforming features 97
4.3.1 Common feature transforms 99
4.3.2 Transforming concepts 102
4.4 Selecting features 103
4.5 Structuring feature code 105
4.5.1 Feature generators 105
4.5.2 Feature set composition 109
4.6 Applications 113
4.7 Reactivities 113
Chapter 5: Learning models 116
5.1 Implementing learning algorithms 117
5.1.1 Bayesian modeling 119
5.1.2 Implementing Naive Bayes 121
5.2 Using MLlib 125
5.2.1 Building an ML pipeline 125
5.2.2 Evolving modeling techniques 130
5.3 Building facades 132
5.3.1 Learning artistic style 133
5.4 Reactivities 138
Chapter 6: Evaluating models 140
6.1 Detecting fraud 141
6.2 Holding out data 142
6.3 Model metrics 145
6.4 Testing models 150
6.5 Data leakage 152
6.6 Recording provenance 153
6.7 Reactivities 155
Chapter 7: Publishing models 158
7.1 The uncertainty of farming 159
7.2 Persisting models 159
7.3 Serving models 164
7.3.1 Microservices 164
7.3.2 Akka HTTP 165
7.4 Containerizing applications 167
7.5 Reactivities 170
Chapter 8: Responding 172
8.1 Moving at the speed of turtles 173
8.2 Building services with tasks 173
8.3 Predicting traffic 176
8.4 Handling failure 180
8.5 Architecting response systems 183
8.6 Reactivities 185
Part 3: Operating a machine learning system 188
Chapter 9: Delivering 190
9.1 Shipping fruit 191
9.2 Building and packaging 192
9.3 Build pipelines 193
9.4 Evaluating models 194
9.5 Deploying 195
9.6 Reactivities 198
Chapter 10: Evolving intelligence 200
10.1 Chatting 200
10.2 Artificial intelligence 201
10.3 Reflex agents 201
10.4 Intelligent agents 203
10.5 Learning agents 204
10.6 Reactive learning agents 208
10.6.1 Reactive principles 208
10.6.2 Reactive strategies 209
10.6.3 Reactive machine learning 209
10.7 Reactivities 209
10.7.1 Libraries 210
10.7.2 System data 211
10.8 Reactive explorations 213
10.8.1 Users 213
10.8.2 System dimensions 214
10.8.3 Applying reactive principles 215
Appendix: Getting set up 218
Scala 218
Git code repository 218
sbt 218
Spark 219
Couchbase 219
Docker 219
index 220
Symbols 220
A 220
B 220
C 220
D 220
E 221
F 221
G 221
H 221
I 221
J 221
K 221
L 222
M 222
N 222
O 222
P 222
Q 222
R 223
S 223
T 223
U 223
V 223
W 223
Z 223

Summary Machine Learning Systems: Designs that scale is an example-rich guide that teaches you how to implement reactive design solutions in your machine learning systems to make them as reliable as a well-built web app. Foreword by Sean Owen, Director of Data Science, Cloudera Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology If you're building machine learning models to be used on a small scale, you don't need this book. But if you're a developer building a production-grade ML application that needs quick response times, reliability, and good user experience, this is the book for you. It collects principles and practices of machine learning systems that are dramatically easier to run and maintain, and that are reliably better for users. About the Book Machine Learning Systems: Designs that scale teaches you to design and implement production-ready ML systems. You'll learn the principles of reactive design as you build pipelines with Spark, create highly scalable services with Akka, and use powerful machine learning libraries like MLib on massive datasets. The examples use the Scala language, but the same ideas and tools work in Java, as well. What's Inside Working with Spark, MLlib, and Akka Reactive design patterns Monitoring and maintaining a large-scale system Futures, actors, and supervision About the Reader Readers need intermediate skills in Java or Scala. No prior machine learning experience is assumed. About the Author Jeff Smith builds powerful machine learning systems. For the past decade, he has been working on building data science applications, teams, and companies as part of various teams in New York, San Francisco, and Hong Kong. He blogs (https: //medium.com/@jeffksmithjr), tweets (@jeffksmithjr), and speaks (www.jeffsmith.tech/speaking) about various aspects of building real-world machine learning systems. Table of Contents PART 1 - FUNDAMENTALS OF REACTIVE MACHINE LEARNING Learning reactive machine learning Using reactive tools PART 2 - BUILDING A REACTIVE MACHINE LEARNING SYSTEM Collecting data Generating features Learning models Evaluating models Publishing models Responding PART 3 - OPERATING A MACHINE LEARNING SYSTEM Delivering Evolving intelligence

Machine learning applications autonomously reason about data at massive scale. It's important that they remain responsive in the face of failure and changes in load. But machine learning systems are different than other applications when it comes to testing, building, deploying, and monitoring.
Reactive Machine Learning Systems teaches readers how to implement reactive design solutions in their machine learning systems to make them as reliable as a well-built web app. Using Scala and powerful frameworks such as Spark, MLlib, and Akka, they'll learn to quickly and reliably move from a single machine to a massive cluster.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Key Features: · Example-rich guide · Step-by-step guide · Move from single-machine to massive cluster Readers should have intermediate skills in Java or Scala. No previous machine learning experience is required.

<p>Machine Learning Systems: Designs that scale is an example-rich guide that teaches you how to implement reactive design solutions in your machine learning systems to make them as reliable as a well-built web app.<br></p>

2019-03-03