Build the data foundation that AI actually needs
AI-Ready Data Blueprints: From Raw Data to AI-Driven Innovation is a practical enterprise guide for teams working with generative AI and agentic AI. Instead of treating AI as a standalone layer, this book focuses on the less glamorous but far more important question: how do you prepare data so AI systems can be accurate, secure, governable, and useful in production?
Written by Navnit Shukla, Kien Pham, Srikanth Sopirala, and Harsha Tadiparthi, the book brings together data strategy, governance, orchestration, retrieval, and optimization in a way that reflects how real organizations build AI systems today. It is especially relevant for readers who want implementation-minded guidance rather than abstract AI enthusiasm.
What the book covers
- the core elements of an AI-ready data foundation
- data governance, access control, lineage, and quality management
- semantic layers, knowledge bases, and vector databases
- retrieval-augmented generation and data preparation for GenAI
- security, compliance, responsible AI, and operational orchestration
- production readiness for AI applications that need to scale
A grounded view of enterprise AI
The book’s strength is its practical scope. It traces the path from raw data to AI-driven innovation while paying close attention to the engineering tradeoffs that matter in real deployments: context, consistency, observability, transparency, and control. Readers will also find discussion of how data requirements change as systems move from assistants to agents.
That makes this a strong fit for data engineers, AI specialists, architects, platform teams, and technical leaders who are shaping how generative AI fits into an existing enterprise stack.
Why it stands out
Many AI books emphasize prompting or model selection. This one starts earlier in the pipeline, where long-term AI success is often won or lost: the data layer. If you are planning serious AI work, that perspective is refreshingly useful.
User Reviews
Only logged in customers who have purchased this product may leave a review.
Original price was: $79.99.$39.99Current price is: $39.99.

There are no reviews yet.