Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Shelter Island, NY
Manning Publications Co.
[2018]
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| Subjects: | |
| Links: | https://learning.oreilly.com/library/view/-/9781617293337/?ar |
| 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. 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. |
| Item Description: | Includes index. - Online resource; title from title page (Safari, viewed July 9, 2018) |
| Physical Description: | 1 Online-Ressource (1 volume) illustrations |
| ISBN: | 1617293334 9781617293337 |
Staff View
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| spelling | Smith, Jeff 1982- VerfasserIn aut Machine learning systems designs that scale Jeff Smith Shelter Island, NY Manning Publications Co. [2018] ©2018 1 Online-Ressource (1 volume) illustrations Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes index. - Online resource; title from title page (Safari, viewed July 9, 2018) 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. 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. Machine learning Apprentissage automatique |
| spellingShingle | Smith, Jeff 1982- Machine learning systems designs that scale Machine learning Apprentissage automatique |
| title | Machine learning systems designs that scale |
| title_auth | Machine learning systems designs that scale |
| title_exact_search | Machine learning systems designs that scale |
| title_full | Machine learning systems designs that scale Jeff Smith |
| title_fullStr | Machine learning systems designs that scale Jeff Smith |
| title_full_unstemmed | Machine learning systems designs that scale Jeff Smith |
| title_short | Machine learning systems |
| title_sort | machine learning systems designs that scale |
| title_sub | designs that scale |
| topic | Machine learning Apprentissage automatique |
| topic_facet | Machine learning Apprentissage automatique |
| work_keys_str_mv | AT smithjeff machinelearningsystemsdesignsthatscale |