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Building AI Systems from Scratch [#1011703]
Building AI Systems from Scratch: How Data Becomes Intelligence: Architecture, Training, and Enterprise Deployment
by Vishal Uttam Mane
English | 2026 | ASIN: B0GLHXHRTD | 324 Pages | PDF | 89 MB
Building AI Systems from Scratch
How Data Becomes Intelligence: Architecture, Training, and Enterprise Deployment
AI models don't fail. AI systems do. Most books teach algorithms. Most courses stop at notebooks. This book teaches you how real AI systems are actually built, deployed, monitored, and scaled in the real world.
Building AI Systems from Scratch is a practical, end-to-end guide to designing production-grade AI systems , from raw data ingestion to enterprise deployment used by companies that run AI at scale.
This is not an academic textbook. This is not a "prompt engineering" guide. This is a systems-first, engineering-driven playbook for anyone serious about building AI that works outside the lab .
What you'll learn
✔ How data becomes intelligence, layer by layer
✔ The difference between models, pipelines, and full AI systems
✔ Why most AI projects fail in production, and how to avoid it
✔ How real enterprise AI architectures are designed
✔ Feature engineering, feature stores, and real-time inference
✔ How models actually learn (with math intuition, not confusion)
✔ Training, evaluating, and deploying models at scale
✔ MLOps: CI/CD for ML, model registries, monitoring, drift & retraining
✔ Generative AI systems, RAG pipelines, and LLM safety
✔ Cost optimization, latency engineering, and AI FinOps
✔ Security, governance, explainability, and compliance
✔ How organizations scale AI across teams and products
Every concept is backed by real-world architecture patterns , production trade-offs , and hands-on implementations .
Hands-on & practical
This book includes:End-to-end enterprise AI system architectureReal data ingestion, validation, and versioning pipelinesFeature stores, online/offline features, and drift handlingModel training from scratch (NumPy → PyTorch)Distributed training and GPU-aware designFastAPI-based inference servicesKubernetes & cloud-ready deployment patternsMonitoring dashboards, alerting, and rollback strategiesA full production-grade AI repository structure
You don't just read this book; you build along with it .
Who this book is for
This book is ideal for:Machine Learning EngineersAI Engineers & ArchitectsBackend / Platform Engineers moving into AIData Scientists transitioning to productionStartup founders building AI productsEngineering managers leading AI teamsAnyone preparing for AI system design interviews
If you already know Python and basic ML, this book will take you from "model builder" to "AI systems engineer."
Why this book is different
Most AI books stop at training a model . This book starts there, and goes all the way to enterprise deployment, monitoring, governance, and scale .
AI is no longer about models. AI is about systems.
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