The Machine Learning System Design Bible: The Definitive Guide to Cracking System Design Interviews for ML Engineers - Insider Secrets to Gain an Unfa
暫譯: 機器學習系統設計聖經：破解機器學習工程師系統設計面試的權威指南 - 獲得無法抗拒的內部秘訣
Shelwick, Trevor
- 出版商: Independently Published
- 出版日期: 2025-03-22
- 售價: $1,220
- 貴賓價: 9.5 折 $1,159
- 語言: 英文
- 頁數: 280
- 裝訂: Quality Paper - also called trade paper
- ISBN: 9798315112440
- ISBN-13: 9798315112440
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相關分類:
Machine Learning、面試技巧
海外代購書籍(需單獨結帳)
相關主題
商品描述
Struggling with ML System Design Interviews?
Want to Build Scalable AI Systems That Actually Work?
ML system design interviews are among the toughest technical challenges in the industry. You're expected to architect complex, scalable AI systems-balancing accuracy, latency, and deployment trade-offs-all within 45 minutes, with no room for hesitation.
Most candidates don't fail because they lack ML knowledge-they fail because they don't know how to structure their answers, justify trade-offs, or think like an ML system designer under pressure.
This book gives you a proven, step-by-step framework to break down any ML system design problem, avoid common pitfalls, and confidently design real-world AI architectures that scale.
What You'll Discover Inside:
Want to Build Scalable AI Systems That Actually Work?
ML system design interviews are among the toughest technical challenges in the industry. You're expected to architect complex, scalable AI systems-balancing accuracy, latency, and deployment trade-offs-all within 45 minutes, with no room for hesitation.
Most candidates don't fail because they lack ML knowledge-they fail because they don't know how to structure their answers, justify trade-offs, or think like an ML system designer under pressure.
This book gives you a proven, step-by-step framework to break down any ML system design problem, avoid common pitfalls, and confidently design real-world AI architectures that scale.
What You'll Discover Inside:
商品描述(中文翻譯)
在機器學習系統設計面試中掙扎嗎？
想要建立實際可行的可擴展 AI 系統嗎？
機器學習系統設計面試是業界中最具挑戰性的技術考驗之一。你需要在45 分鐘內架構出複雜且可擴展的 AI 系統，平衡準確性、延遲和部署的權衡，而且沒有猶豫的空間。
大多數候選人並不是因為缺乏機器學習知識而失敗，而是因為他們不知道如何結構化自己的回答、合理化權衡，或在壓力下像機器學習系統設計師一樣思考。
這本書提供了一個經過驗證的逐步框架，幫助你拆解任何機器學習系統設計問題，避免常見的陷阱，並自信地設計可擴展的實際 AI 架構。
你將在書中發現：
