Reliable Machine Learning: Applying Sre Principles to ML in Production (Paperback)

Whether you're part of a small startup or a multinational corporation, this practical book shows data scientists, software and site reliability engineers, product managers, and business owners how to run ML reliably, effectively, and accountably within your organization. You'll gain insight into everything from how to do model monitoring in production to how to run a well-tuned model development team in a product organization.

By applying an SRE mindset to machine learning, authors and engineering professionals Cathy Chen, Kranti Parisa, Niall Richard Murphy, D. Sculley, Todd Underwood, and featured guest authors show you how to run an efficient and reliable ML system. Whether you want to increase revenue, optimize decision making, solve problems, or understand and influence customer behavior, you'll learn how to perform day-to-day ML tasks while keeping the bigger picture in mind.

You'll examine:

• What ML is: how it functions and what it relies on
• Conceptual frameworks for understanding how ML loops work
• Effective productionization, and how it can be made easily monitorable, deployable, and operable
• Why ML systems make production troubleshooting more difficult, and how to get around them
• How ML, product, and production teams can communicate effectively

無論您是小型初創企業還是跨國公司的一部分，這本實用書籍將向數據科學家、軟件和網站可靠性工程師、產品經理和企業主展示如何在組織內可靠、有效和負責任地運行機器學習。您將獲得有關如何在生產環境中進行模型監控以及如何在產品組織中運行調校良好的模型開發團隊的洞察。

通過將可靠性工程思維應用於機器學習，作者和工程專業人員Cathy Chen、Kranti Parisa、Niall Richard Murphy、D. Sculley、Todd Underwood以及特邀嘉賓作者向您展示如何運行高效可靠的機器學習系統。無論您是想增加收入、優化決策、解決問題還是了解並影響客戶行為，您都將學習如何在日常機器學習任務中保持整體視野。

您將研究以下內容：

- 機器學習的定義：其功能和依賴
- 理解機器學習循環工作的概念框架
- 有效的生產化，以及如何使其易於監控、部署和操作
- 為什麼機器學習系統使生產環境故障排除更加困難，以及如何解決這些問題
- 機器學習、產品和生產團隊如何有效溝通