AI-Ready Data Blueprints (Final) by Navnit Shukla (.ePUB)+
File Size: 16.2 MB
AI-Ready Data Blueprints: From Raw Data to AI-Driven Innovation (Final Release) by Navnit Shukla, Kien Pham, Srikanth Sopirala, Harsha Tadiparthi
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Overview: 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. This book follows the journey your data takes—from raw, messy, and scattered to AI-ready, governed, and production-grade. We start by laying out why generative AI demands a fundamentally different approach to data than traditional analytics or Machine Learning. It’s not just about cleaning up tables anymore. It’s about preserving meaning, modeling relationships, and building systems that can reason, not just retrieve. From there, we walk you through a comprehensive framework for AI-ready data, covering everything from capturing business logic and context to ensuring quality and consistency to managing the security and compliance challenges that come with putting AI into the real world. We explore the nuts and bolts of knowledge bases, vector databases, chunking strategies, and retrieval optimization, because the research is clear: how you prepare your data matters five to six times more than which model you choose. We also confront the challenges you’ll face after you develop a working prototype, delving into topics such as production readiness, automated reasoning, intelligent semantic metadata layers, and the emerging landscape of agentic AI platforms. These aren’t abstract concepts. The insights we provide come from real implementations, including organizations managing quadrillions of files accumulated over decades. Blueprints, architecture diagrams, and sample code are available via the book’s companion website and GitHub repository.
Genre: Non-Fiction > Tech & Devices
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