Machine Learning Engineering in Action
Language: English
Published by Manning Publications Apr 2022, 2022
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
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Add to basketItem description from seller
Neuware - Field-tested tips, tricks, and design patterns for building MachineLearning projects that are deployable, maintainable, and secure from concept toproduction.In Machine Learning Engineering inAction, you will learn: - Evaluatingdata science problems to find the most effective solution - Scopinga machine learning project for usage expectations and budget - Processtechniques that minimize wasted effort and speed up production - Assessinga project using standardized prototyping work and statistical validation - Choosingthe right technologies and tools for your project - Makingyour codebase more understandable, maintainable, and testable - Automatingyour troubleshooting and logging practices Databricks solutions architect BenWilson lays out an approach to building deployable, maintainable productionmachine learning systems. YouGÇÖll adopt software development standards thatdeliver better code management, and make it easier to test, scale, and evenreuse your machine learning code.
Seller Inventory # 9781617298714
- Title
- Machine Learning Engineering in Action
- Author
- Ben Wilson
- Publisher
- Manning Publications Apr 2022
- Publication year
- 2022
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1617298719
- ISBN 13
- 9781617298714
- Item weight
- 1,032 grams
- Dimensions
- 232x185x32 mm
Machine Learning Engineering in Action lays out an approach to building deployable, maintainable production machine learning systems. You will adopt software development standards that deliver better code management, and make it easier to test, scale, and even reuse your machine learning code!
You will learn how to plan and scope your project, manage cross-team logistics that avoid fatal communication failures, and design your code's architecture for improved resilience. You will even discover when not to use machine learning―and the alternative approaches that might be cheaper and more effective. When you're done working through this toolbox guide, you will be able to reliably deliver cost-effective solutions for organizations big and small alike.
Following established processes and methodology maximizes the likelihood that your machine learning projects will survive and succeed for the long haul. By adopting standard, reproducible practices, your projects will be maintainable over time and easy for new team members to understand and adapt.
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