Machine Learning Systems: Designs That Scale
| AUTHOR | Smith, Jeff |
| PUBLISHER | Manning Publications (07/08/2018) |
| PRODUCT TYPE | Paperback (Paperback) |
Description
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
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
PART 2 - BUILDING A REACTIVE MACHINE LEARNING SYSTEM
PART 3 - OPERATING A MACHINE LEARNING SYSTEM
- 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
Show More
Product Format
Product Details
ISBN-13:
9781617293337
ISBN-10:
1617293334
Binding:
Paperback or Softback (Trade Paperback (Us))
Content Language:
English
More Product Details
Page Count:
224
Carton Quantity:
40
Product Dimensions:
7.30 x 0.40 x 9.20 inches
Weight:
0.80 pound(s)
Feature Codes:
Index,
Price on Product,
Illustrated
Country of Origin:
US
Subject Information
BISAC Categories
Computers | Computer Science
Computers | Artificial Intelligence - Natural Language Processing
Computers | Programming - Algorithms
Dewey Decimal:
006.31
Descriptions, Reviews, Etc.
publisher marketing
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
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
PART 2 - BUILDING A REACTIVE MACHINE LEARNING SYSTEM
PART 3 - OPERATING A MACHINE LEARNING SYSTEM
- 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
Show More
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