Название: Effective Machine Learning Teams: Best Practices for ML Practitioners (Fifth Early Release)Автор: David Tan, Ada Leung
Издательство: O’Reilly Media, Inc.
Год: 2023-07-18
Страниц: 296
Язык: английский
Формат: pdf, epub, mobi
Размер: 10.1 MB
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
Who Is This Book For:
Whether you’re a ML practitioner in academia, enterprise organizations, start-ups, scale-ups, or consulting, the principles and practices in this book can help you and your team identify opportunities for improvement and be more effective at what you do.
Data scientists and ML engineers
The job scope of a data scientist has evolved over the past few years. Instead of purely focusing on modeling techniques and data analysis, we’re seeing expectations (implicit or explicit) that one needs the capabilities of a full-stack data scientist: data wrangling, ML engineering, MLOps, business case formulation, among others. This book will elaborate on the essential capabilities (beyond ML) which are required in designing and delivering ML solutions in the real world. We have presented the principles, practices and hands-on exercises in this book to various groups of ML practitioners (data scientists, ML engineers, PhD students, software engineers) over several years, and we’ve consistently received positive feedback.
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