Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications (Paperback)

Many tutorials show you how to develop ML systems from ideation to deployed models. But with constant changes in tooling, those systems can quickly become outdated. Without an intentional design to hold the components together, these systems will become a technical liability, prone to errors and be quick to fall apart.

In this book, Chip Huyen provides a framework for designing real-world ML systems that are quick to deploy, reliable, scalable, and iterative. These systems have the capacity to learn from new data, improve on past mistakes, and adapt to changing requirements and environments. Youà Ã?Â[ ll learn everything from project scoping, data management, model development, deployment, and infrastructure to team structure and business analysis.

• Learn the challenges and requirements of an ML system in production
• Build training data with different sampling and labeling methods
• Leverage best techniques to engineer features for your ML models to avoid data leakage
• Select, develop, debug, and evaluate ML models that are best suit for your tasks
• Deploy different types of ML systems for different hardware
• Explore major infrastructural choices and hardware designs
• Understand the human side of ML, including integrating ML into business, user experience, and team structure

許多教學都會教你如何從構想到部署模型開發機器學習系統。但是由於工具不斷變化，這些系統很快就會過時。如果沒有有意識地設計來將組件結合在一起，這些系統將成為技術負擔，容易出錯並迅速崩潰。

在這本書中，Chip Huyen提供了一個框架，用於設計快速部署、可靠、可擴展且迭代的真實世界機器學習系統。這些系統具有從新數據中學習、改進過去錯誤並適應變化需求和環境的能力。您將從項目範圍、數據管理、模型開發、部署和基礎設施到團隊結構和業務分析等方面學到一切。

- 了解在生產中機器學習系統的挑戰和要求
- 使用不同的採樣和標記方法構建訓練數據
- 利用最佳技術為您的機器學習模型設計特徵，以避免數據泄漏
- 選擇、開發、調試和評估最適合您任務的機器學習模型
- 部署不同類型的機器學習系統以適應不同的硬件
- 探索主要的基礎設施選擇和硬件設計
- 了解機器學習的人性化方面，包括將機器學習整合到業務、用戶體驗和團隊結構中

Chip Huyen (https: //huyenchip.com) is an engineer and founder who develops infrastructure for real-time machine learning. Through her work at Netflix, NVIDIA, Snorkel AI, and her current startup, she has helped some of the world's largest organizations develop and deploy machine learning systems. She is the founder of a startup that focuses on real-time machine learning.

In 2017, she created and taught the Stanford course TensorFlow for Deep Learning Research. She is currently teaching CS 329S: Machine Learning Systems Design at Stanford. This book is based on the course's lecture notes.

She is also the author of four Vietnamese books that have sold more than 100,000 copies. The first two books belong to the series Xach ba lo len va Di (Quang Van 2012, 2013). The first book in the series was the #1 best-selling book of 2012 on Tiki.vn. The series was among FAHASA's Top 10 Readers Choice Books in 2014.

Chip's expertise is in the intersection of software engineering and machine learning. LinkedIn included her among the 10 Top Voices in Software Development in 2019, and Top Voices in Data Science & AI in 2020.

Chip Huyen（https://huyenchip.com）是一位工程師和創業者，專注於開發實時機器學習基礎設施。通過在Netflix、NVIDIA、Snorkel AI和她目前的創業公司的工作，她幫助了一些全球最大的組織開發和部署機器學習系統。她是一家專注於實時機器學習的創業公司的創始人。

在2017年，她創建並教授了斯坦福大學的TensorFlow深度學習研究課程。她目前在斯坦福大學教授CS 329S：機器學習系統設計。本書是基於該課程的講義。

她還是四本越南書籍的作者，銷量超過10萬冊。前兩本書屬於系列作品《Xach ba lo len va Di》（Quang Van 2012, 2013）。該系列作品的第一本書是2012年Tiki.vn暢銷書籍榜首。該系列作品在2014年FAHASA讀者選書中排名前十。

Chip的專業領域是軟件工程和機器學習的交叉領域。LinkedIn將她列為2019年軟件開發領域的十大聲音之一，並將她列為2020年數據科學和人工智能領域的十大聲音之一。