Effective Machine Learning Teams: Best Practices for ML Practitioners

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

這本實用指南將幫助資料科學家和機器學習工程師獲得加速交付機器學習解決方案所需的寶貴技能和技巧。作者David Tan和Ada Leung來自Thoughtworks，以實際且簡單的方式展示如何彌合資料科學和精實軟體交付之間的差距。他們基於多個真實世界的資料和機器學習專案經驗，提供了經過驗證的技巧，幫助團隊避免在機器學習領域中常見的陷阱，以便更快、更可靠地進行迭代。憑藉這些技巧，資料科學家和機器學習工程師可以克服阻力，並在交付機器學習解決方案時獲得流暢的體驗。

本書將教你如何：
- 應用軟體工程實踐，如撰寫自動化測試、容器化開發環境和重構有問題的程式碼庫
- 應用MLOps和CI/CD實踐，加速實驗週期並提高機器學習解決方案的可靠性
- 設計可維護和可演進的機器學習解決方案，以敏捷方式應對變化
- 應用交付和產品實踐，逐步提高為用戶建立正確產品的機會
- 使用智能程式碼編輯器功能，更有效地編寫程式碼