From Model to Product

Foundations for Practical AI Engineering

By Dr. Dulan Dias · 2025

£21.99

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Artificial intelligence is easy to demonstrate. Building an AI system that actually works in the real world is much harder. This book addresses the core challenge: the hard part is not the model - it's everything around it. From problem framing and architecture to deployment and production monitoring, this is the practical guide for engineers moving beyond notebooks.

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What's inside

System design principles for production AI
Deployment using FastAPI and containerisation
Monitoring, cost management, and observability
Handling real-world constraints: reliability, concurrency, latency
Classical ML, computer vision, and LLM-based approaches
Team collaboration and responsible AI governance
Edge-and-cloud hybrid architectures
Problem framing: knowing when NOT to use AI

Chapter overview

01The Production Gap - why most AI projects never ship
02Problem Framing - defining the right problem before writing code
03Data Pipelines - engineering data flows for reliability
04Model Architecture - selecting and designing models for production
05Serving & APIs - FastAPI, containerisation, and deployment patterns
06Monitoring & Observability - knowing when your system breaks
07Cost Management - running AI systems sustainably at scale
08Team Structures - organising for cross-functional AI delivery