Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications (Paperback)
暫譯: 設計機器學習系統：生產就緒應用的迭代過程 (平裝本)

Huyen, Chip

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

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商品描述

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 But with constant changes in tooling, those systems can quickly become outdated.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

商品描述(中文翻譯)

許多教程展示了如何從構思到部署模型開發機器學習（ML）系統。但隨著工具的不斷變化，這些系統可能會迅速過時。如果沒有有意識的設計來將各個組件連接在一起，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 提供了一個設計現實世界機器學習系統的框架，這些系統能夠快速部署、可靠、可擴展且具迭代性。這些系統具備從新數據中學習、改進過去錯誤以及適應變化需求和環境的能力。您將學習從項目範疇、數據管理、模型開發、部署和基礎設施到團隊結構和商業分析的所有內容。

- 了解生產環境中機器學習系統的挑戰和需求
- 使用不同的抽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工程特徵，以避免數據洩漏
- 選擇、開發、調試和評估最適合您任務的機器學習模型
- 為不同硬體部署不同類型的機器學習系統
- 探索主要的基礎設施選擇和硬體設計
- 理解機器學習的人性面，包括將機器學習整合到商業中、用戶體驗和團隊結構

作者簡介

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.

作者簡介(中文翻譯)

Chip Huyen (https://huyenchip.com) 是一位工程師和創始人，專注於開發即時機器學習的基礎設施。透過她在 Netflix、NVIDIA、Snorkel AI 的工作，以及她目前的創業公司，她幫助一些全球最大的組織開發和部署機器學習系統。她是專注於即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課程《TensorFlow for Deep Learning Research》。她目前在斯坦福教授 CS 329S：機器學習系統設計。這本書是基於該課程的講義。

她也是四本越南書籍的作者，這些書籍的銷量超過 100,000 本。前兩本書屬於系列《Xach ba lo len va Di》（Quang Van 2012, 2013）。該系列的第一本書在 2012 年是 Tiki.vn 的暢銷書第一名。該系列在 2014 年被評選為 FAHASA 的十大讀者選擇書籍之一。

Chip 的專業領域是軟體工程與機器學習的交集。LinkedIn 在 2019 年將她列為軟體開發的十大頂尖聲音之一，並在 2020 年將她列為數據科學與人工智慧的頂尖聲音之一。

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