From Model to Product: Foundations for Practical AI Engineering (Softcover)

Description

From Model to Product: Foundations for Practical AI Engineering

Artificial intelligence is easy to demonstrate.
Building an AI system that actually works in the real world is much harder.

Most books teach models, algorithms, or libraries.
This book teaches how to turn those pieces into reliable, production-ready AI products.

From Model to Product is a practical guide to AI engineering, written for developers, researchers, and technical professionals who want to move beyond experiments and learn how real systems are designed, built, and deployed.

Instead of focusing on theory alone, the book walks through the full lifecycle of an AI application — from problem definition to architecture, from data pipelines to deployment, from classical machine learning to modern LLM-based systems, and from prototypes to production environments.

You will learn how to think like an AI engineer, not just how to train a model.

Inside the book you will learn how to:

• Frame AI problems correctly before choosing models
• Design data pipelines, APIs, and storage for real systems
• Combine classical ML, computer vision, and LLMs in one product
• Build hybrid edge-and-cloud AI applications
• Deploy models using FastAPI, containers, and modern workflows
• Monitor, scale, and control the cost of AI services
• Handle concurrency, reliability, and real-world constraints
• Work effectively in AI product teams
• Understand risk, governance, and responsible AI engineering
• Think like a system architect, not just a model builder

The book includes a complete end-to-end project that demonstrates how multiple AI techniques can be combined into a practical application, with clear explanations of every design decision along the way.

This is not a beginner coding tutorial.
It is a guide for people who want to build AI systems that survive outside the notebook.