Name: Machine Learning Systems: Designs That Scale
Price: 1003.00 ZAR
Availability: InStock
Author: Jeff Smith
ISBN: 9781617293337

portada Machine Learning Systems: Designs That Scale

Machine Learning Systems: Designs That Scale

Type

Physical Book

Author

Jeff Smith

Publisher

Manning Publications

Year

2018

Language

English

Pages

224

Format

Paperback

ISBN13

9781617293337

Edition No.

Synopsis "Machine Learning Systems: Designs That Scale "

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 patternsMonitoring and maintaining a large-scale systemFutures, 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 LEARNINGLearning reactive machine learningUsing reactive toolsPART 2 - BUILDING A REACTIVE MACHINE LEARNING SYSTEMCollecting dataGenerating featuresLearning modelsEvaluating modelsPublishing modelsRespondingPART 3 - OPERATING A MACHINE LEARNING SYSTEMDeliveringEvolving intelligence

Jeff Smith

(Author)

View Author's Page

Jeff Smith nació en McKees Rocks (Pensilvania) y creció en Columbus (Ohio), donde vive en la actualidad. Es conocido principalmente por la serie Bone, que empezó a autoeditar en 1991 y que finalizaría en 2004. Bone ha sido publicada íntegramente en castellano por Astiberri tanto en el blanco y negro original como en color, así como en edición de bolsillo.

Su creación más reciente es RASL, una serie de ciencia ficción dura y descarnada sobre un ladrón de arte interdimensional, que consta de dos tomos, ambos publicados por Astiberri.

Asimismo, Smith prepara la adaptación cinematográfica de Bone, cuyos derechos fueron adquiridos por la Warner en 2008. La obra de Jeff Smith ha sido objeto de varias exposiciones antológicas en el Wexner Center for the Arts y en la Universidad Estatal de Ohio.

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