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Effective Machine Learning Teams Best Practices for Ml Practitioners
Effective Machine Learning Teams Best Practices for Ml Practitioners
Effective Machine Learning Teams Best Practices for Ml Practitioners
Effective Machine Learning Teams Best Practices for Ml Practitioners
Effective Machine Learning Teams Best Practices for Ml Practitioners
Effective Machine Learning Teams Best Practices for Ml Practitioners
Effective Machine Learning Teams Best Practices for Ml Practitioners
Effective Machine Learning Teams Best Practices for Ml Practitioners
Effective Machine Learning Teams Best Practices for Ml Practitioners
Effective Machine Learning Teams Best Practices for Ml Practitioners
Effective Machine Learning Teams Best Practices for Ml Practitioners
Effective Machine Learning Teams Best Practices for Ml Practitioners
Effective Machine Learning Teams Best Practices for Ml Practitioners
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Effective Machine Learning Teams Best Practices for Ml Practitioners

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220,000₫

Effective Machine Learning Teams Best Practices for Ml Practitioners

Sách gia công, Bìa mềm
 
 
Gain the valuable skills and techniques you need to
accelerate the delivery of machine learning solutions. With this
practical guide, data scientists and ML engineers will learn how to
bridge the gap between data science and Lean software delivery in a
practical and simple way. David Tan and Ada Leung from Thoughtworks show
you how to apply time-tested software engineering skills and Lean
delivery practices that will improve your effectiveness in ML projects.
 
Based
on the authors’ experience across multiple real-world data and ML
projects, the proven techniques in this book will help teams avoid
common traps in the ML world, so you can iterate more quickly and
reliably. With these techniques, data scientists and ML engineers can
overcome friction and experience flow when delivering machine learning
solutions.
 
This book shows you how to:
• Apply engineering
practices such as writing automated tests, containerizing development
environments, and refactoring problematic code bases
• Apply MLOps and CI/CD practices to accelerate experimentation cycles and improve reliability of ML solutions
• Design maintainable and evolvable ML solutions that allow you to respond to changes in an agile fashion
• Apply delivery and product practices to iteratively improve your odds of building the right product for your users
• Use intelligent code editor features to code more effectively
 
 
Year:2024
 
Edition:1
 
Publisher:O'Reilly Media / O'Reilly & Associates Inc.
 
Language:english
 
Pages:402
 

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