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

About the Authors

Navnit Shukla is an AWS Specialist Solutions Architect focusing on Analytics. He helps clients derive insights from data and is the author of Data Wrangling on AWS.

Kien Pham is an AWS Principal Solutions Architect supporting Digital Native Business. Kien has over 10 years of software engineering experience.

Srikanth Sopirala is a Principal Analytics & AI Specialist Solutions Architect at AWS. He helps organizations navigate modern data management with secure, scalable solutions.

Harsha Tadiparthi is a Principal Data & AI Strategist at AWS with over 16 years of experience. He specializes in analytics, AI, and data governance and is a lead author of Unleashing Data Governance and Data Mesh in the Age of Generative AI.

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