AI-Ready Data Blueprints: From Raw Data to AI-Driven Innovation
Date: June 16th, 2026
ISBN: 9798341631793
Language: English
Number of pages: 290 pages
Format: EPUB
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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
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
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