Machine Learning Production Systems: Engineering Machine Learning Models and Pipelines
Crowe, Robert, Hapke, Hannes, Caveness, Emily
- 出版商: O'Reilly
- 出版日期: 2024-11-05
- 定價: $2,750
- 售價: 9.5 折 $2,613
- 貴賓價: 9.0 折 $2,475
- 語言: 英文
- 頁數: 472
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1098156013
- ISBN-13: 9781098156015
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相關分類:
Machine Learning
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相關主題
商品描述
Using machine learning for products, services, and critical business processes is quite different from using ML in an academic or research setting--especially for recent ML graduates and those moving from research to a commercial environment. Whether you currently work to create products and services that use ML, or would like to in the future, this practical book gives you a broad view of the entire field.
Authors Robert Crowe, Hannes Hapke, Emily Caveness, Di Zhu, and Catherine Nelson help you identify topics that you can dive into deeper, along with reference materials and tutorials that teach you the details. You'll learn the state of the art of machine learning engineering, including a wide range of topics such as modeling, deployment, and MLOps. You'll learn the basics and advanced aspects to understand the production ML lifecycle.
This book provides four in-depth sections that cover all aspects of machine learning engineering:
- Data: collecting, labeling, validating, automation, and data preprocessing; data feature engineering and selection; data journey and storage
- Modeling: high performance modeling; model resource management techniques; model analysis and interoperability; neural architecture search
- Deployment: model serving patterns and infrastructure for ML models and LLMs; management and delivery; monitoring and logging
- Productionalizing: ML pipelines; classifying unstructured texts and images; genAI model pipelines
商品描述(中文翻譯)
使用機器學習於產品、服務和關鍵商業流程,與在學術或研究環境中使用機器學習有著相當大的不同,尤其是對於最近的機器學習畢業生以及那些從研究轉向商業環境的人來說。無論您目前是否在創建使用機器學習的產品和服務,或是未來希望這樣做,本書都將為您提供整個領域的廣泛視野。
作者 Robert Crowe、Hannes Hapke、Emily Caveness、Di Zhu 和 Catherine Nelson 將幫助您識別可以深入探討的主題,並提供參考資料和教學,讓您了解細節。您將學習到機器學習工程的最新技術,包括建模、部署和 MLOps 等廣泛主題。您將學習到生產機器學習生命周期的基本和進階方面。
本書提供四個深入的部分,涵蓋機器學習工程的各個方面:
- **數據:** 收集、標記、驗證、自動化和數據預處理;數據特徵工程和選擇;數據旅程和存儲
- **建模:** 高效能建模;模型資源管理技術;模型分析和互操作性;神經架構搜索
- **部署:** 模型服務模式和機器學習模型及大型語言模型的基礎設施;管理和交付;監控和日誌記錄
- **生產化:** 機器學習管道;對非結構化文本和圖像進行分類;生成式 AI 模型管道