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Artículo: AMZ-B0G99GM4RF
Architecting AI Data Systems: Advanced Concepts for Senior Software Engineers
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Kindle
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0.20 kg
0.20 kg
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Amazon
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USA
Sobre este producto
- About the BookModern data systems are no longer just about storage or dashboards. They are intelligent, distributed, and deeply integrated with machine learning and AI. This book is a practical guide to building end-to-end data platforms that power real-time intelligence, semantic search, and AI-driven decision-making at scale.Spanning infrastructure, retrieval, and visualization, the book connects the full lifecycle of data, from how it is stored and processed, to how it is retrieved by machine learning systems, to how insights are delivered to humans. It bridges theory with real-world architectures, industry case studies, and production-grade design patterns used by data-driven organizations.Readers will learn how today’s data stacks evolved into lakehouses, streaming pipelines, and MLOps platforms; how machine learning transformed search through embeddings, vector databases, and retrieval-augmented generation (RAG); and how AI is reshaping analytics through natural-language interfaces, automated insights, and intelligent visualization. The book also addresses challenges such as data debt, governance at scale, and human factors behind effective data storytelling.Written for data engineers, ML engineers, architects, analytics leaders, and technically curious executives, this book provides a clear mental model for designing systems that are scalable, intelligent, and usable today and in the future.You will learn how to:Design scalable data and AI infrastructure for batch, streaming, and ML workloadsBuild modern retrieval systems using embeddings, vector databases, and LLMsEnable semantic, multimodal, and generative search experiencesCreate interactive, AI-assisted visualizations and production analyticsAvoid Data Debt and align architecture with long-term business outcomesIf you are responsible for turning data into intelligence and intelligence into action, this book provides the blueprint.About the Authors Sayantan GhoshSayantan Ghosh is an award-winning senior engineering leader with deep expertise in building large-scale AI and Data platforms that power products used by billions globally. With leadership roles across Meta, Uber, LinkedIn, and eBay, he has driven some of the industry’s most influential machine learning and data infrastructure initiatives, including Uber’s Michelangelo ML Platform, Meta’s FBLearner ML Platform and LinkedIn’s Feed Data Platform, which power multi-billion dollar lines of business like Uber Eats, Instagram Reels, FB Newsfeed, FB Marketplace etc. An alumnus of IIT Kharagpur & a Snr IEEE member, Sayantan holds a widely cited US patent, is a published author, serves on program committees of leading conferences and is a frequent invited speaker at international venues.Ashish Shubham Ashish Shubham is a Vice President of Engineering at ThoughtSpot, where he has spent a decade architecting industry-defining AI and analytics products for global enterprises. He is the original creator of ThoughtSpot Embedded, one of the leading embedded analytics platforms powering data-driven experiences inside hundreds of applications. Ashish also co-architected ThoughtSpot’s flagship AI Analyst, Spotter, and pioneered the company’s first Agentic MCP Server integration, a breakthrough that is redefining how AI agents securely interact with enterprise data. He holds multiple patents, has published widely read technical articles, contributes to global technical committees & frequently delivers talks at industry forums.ForewordThe book opens with an insightful foreword by Amit Prakash, Author Elements of Programming Interviews, Co-founder & CTO ThoughtSpot, Founder & CEO AmpUp.ai
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