Основні

Виробник

To Be

Користувальницькі характеристики

Друк	чорно-білий
Мова	English
Папір	білий, офсет
Стан	нова книга

Machine Learning Systems: Designs that scale, Jeff Smith купить книгу в Україні

Обкладинка - м"яка

Рік видання - 2018

Кількість сторінок - 224

Папір - білий, офсет

Про книгу Machine Learning Systems: Designs that scale, Jeff Smith

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 Systems: Designs that scale, Jeff Smith

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