AI-Ready Data Blueprints: From Raw Data to Ai-Driven Innovation (Paperback)
暫譯: AI 準備數據藍圖:從原始數據到 AI 驅動的創新 (平裝本)
Shukla, Navnit, Pham, Kien, Sopirala, Srikanth
- 出版商: O'Reilly
- 出版日期: 2026-06-16
- 定價: $2,800
- 售價: 9.0 折 $2,520
- 語言: 英文
- 頁數: 290
- 裝訂: Quality Paper - also called trade paper
- ISBN: 9798341631793
- ISBN-13: 9798341631793
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相關分類:
Prompt Engineering
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相關主題
商品描述
Companies innovating with generative AI understand that having the right data foundation is critical for success and profitability. To best position themselves for long-term success, organizations must prioritize investments in data and AI governance. AI-Ready Data Blueprints is your map to connecting data strategy, GenAI, and ethical practices to build and scale truly effective solutions.
Taking a comprehensive, cloud-agnostic approach focused on real-world business challenges, seasoned data and AI experts Navnit Shukla, Kien Pham, Srikanth Sopirala, and Harsha Tadiparthi share actionable insights to guide you in designing and implementing effective data-centric GenAI systems. Whether you're new to GenAI or are already focusing on optimizing it for accuracy, speed, or both, the principles shared in this book will empower you to excel in all your AI endeavors.
- Identify the key elements of a solid data foundation for generative AI
- Apply data governance and orchestration techniques to ensure high data quality, access control, and proper data lineage for reliable AI systems
- Optimize GenAI applications through prompt engineering, fine-tuning, and retrieval-augmented generation
- Implement security, compliance, and governance measures, including responsible AI practices, transparency, and more
商品描述(中文翻譯)
創新使用生成式人工智慧的公司了解,擁有正確的數據基礎對於成功和盈利至關重要。為了最佳地為長期成功定位,組織必須優先投資於數據和人工智慧治理。AI-Ready Data Blueprints 是您連接數據策略、生成式人工智慧(GenAI)和倫理實踐的地圖,以建立和擴展真正有效的解決方案。
這本書採取全面的、與雲平台無關的方法,專注於現實世界的商業挑戰,資深的數據和人工智慧專家 Navnit Shukla、Kien Pham、Srikanth Sopirala 和 Harsha Tadiparthi 分享可行的見解,指導您設計和實施以數據為中心的有效生成式人工智慧系統。無論您是剛接觸生成式人工智慧,還是已經專注於優化其準確性、速度或兩者兼具,本書中分享的原則將使您在所有人工智慧的努力中脫穎而出。
- 識別生成式人工智慧堅實數據基礎的關鍵要素
- 應用數據治理和協調技術,以確保高數據質量、訪問控制和正確的數據來源,從而建立可靠的人工智慧系統
- 通過提示工程、微調和檢索增強生成來優化生成式人工智慧應用
- 實施安全性、合規性和治理措施,包括負責任的人工智慧實踐、透明度等