Data for AI: Data Infrastructure for Machine Intelligence

The definitive executive blueprint for designing, scaling, and operating high-performance data architectures for machine learning and artificial intelligence. This book bypasses algorithmic theory to focus entirely on the foundational data technologies, pipelines, master data strategies, and ethical governance frameworks that make enterprise AI systems successful.

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274 Pages

12 Chapters

2 Parts Structure

978-1634627290 ISBN

Scott Burk

Kinshuk Dutta

Interactive Architectures & Frameworks

Explore the primary systems designs and operational feedback loops documented throughout the chapters of the book. Click any node to drill down into the underlying concepts.

Chapter 10

1. Event-Driven Data Ingestion

Continuous real-time ingestion pipelines extract raw operational events and log metrics. Employing frameworks like Apache Kafka allows high-throughput buffering, isolating source databases from the strict query pressures of ML model inference engines.

"Without resilient stream buffering at inception, model query scaling directly degrades source database performance."

Interactive Chapter Guide

Step through the structural roadmap of the book. Read chapter summaries, find page references, and explore key takeaways for both theoretical concepts and operations.

Part I: Evolution & Overview

Part II: Operationalizing Data

Part I: AI Evolution and Data Overview Starts on Page 14

Chapter 1: Introduction to Data for AI

Data is the absolute lifeblood of artificial intelligence. While algorithmic research and model science dominate technical press, the true gating factor for corporate AI adoption lies in the structural, storage, and piping architectures. This chapter sets the stage, defining why enterprise data engineering is the actual foundation of AI success.

Key Chapter Insights:

Delineating the three pillars of AI adoption: Computing Power, Algorithmic Progress, and the core pillar of Data Technology.
Why standard transactional database designs fail when subjected to training datasets.
The organizational risk of neglecting data preparation, resulting in high-maintenance downstream failure modes.

"This book is not about algorithms or the theoretical underpinnings of AI. Instead, it is about the practical and tangible components that form the foundation: the data."

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