ML and Generative AI in the Data Lakehouse: Building and Deploying AI Applications at Scale
暫譯: 數據湖倉中的機器學習與生成式人工智慧：大規模構建與部署AI應用程式

Haelen, Bennie

ML and Generative AI in the Data Lakehouse: Building and Deploying AI Applications at Scale
  • 出版商: O'Reilly
  • 出版日期: 2026-07-21
  • 售價: $2,720
  • 貴賓價: 9.5 折 $2,584
  • 語言: 英文
  • 頁數: 445
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1098178491
  • ISBN-13: 9781098178499
  • 相關分類: Large language model

    • 海外代購書籍(需單獨結帳)

商品描述

In today's race to harness generative AI, many teams struggle to integrate these advanced tools into their business systems. While platforms like GPT-4 and Google's Gemini are powerful, they aren't always tailored to specific business needs. This book offers a practical guide to building scalable, customized AI solutions using the full potential of data lakehouse architecture.

Author Bennie Haelen covers everything from deploying ML and GenAI models in Databricks to optimizing performance with best practices. In this must-read for data professionals, you'll gain the tools to unlock the power of large language models (LLMs) by seamlessly combining data engineering and data science to create impactful solutions.

  • Learn to build, deploy, and monitor ML and GenAI models on a data lakehouse architecture using Databricks
  • Leverage LLMs to extract deeper, actionable insights from your business data residing in lakehouses
  • Discover how to integrate traditional ML and GenAI models for customized, scalable solutions
  • Utilize open source models to control costs while maintaining model performance and efficiency
  • Implement best practices for optimizing ML and GenAI models within the Databricks platform

商品描述(中文翻譯)

在當今利用生成式人工智慧的競賽中，許多團隊在將這些先進工具整合到其商業系統中時面臨挑戰。雖然像 GPT-4 和 Google 的 Gemini 等平台功能強大，但它們並不總是針對特定的商業需求進行調整。本書提供了一個實用指南，幫助讀者利用數據湖屋架構的全部潛力來構建可擴展的定制 AI 解決方案。

作者 Bennie Haelen 涵蓋了從在 Databricks 部署機器學習 (ML) 和生成式人工智慧 (GenAI) 模型到使用最佳實踐優化性能的所有內容。在這本數據專業人士必讀的書籍中，您將獲得工具，通過無縫結合數據工程和數據科學來釋放大型語言模型 (LLMs) 的力量，創造有影響力的解決方案。

- 學習如何在數據湖屋架構上使用 Databricks 構建、部署和監控 ML 和 GenAI 模型
- 利用 LLM 從存放在湖屋中的商業數據中提取更深入、可行的見解
- 探索如何整合傳統的 ML 和 GenAI 模型以實現定制的可擴展解決方案
- 利用開源模型來控制成本，同時保持模型的性能和效率
- 在 Databricks 平台內實施最佳實踐以優化 ML 和 GenAI 模型

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