Smith J. - Machine Learning Systems [2018, PDF/EPUB/MOBI, ENG] + Code

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Machine Learning Systems

Год издания: 2018
Автор: Smith J.

Издательство: Manning
ISBN: 978-1617293337
Язык: Английский

Формат: PDF/ePub/mobi
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 224

Описание: 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

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.
[spoiler="Примеры страниц"]

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[spoiler="Оглавление"]
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
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