From Model to Product: Foundations for Practical AI Engineering

There’s a moment most people hit when working with AI.

It usually comes after the excitement.

You’ve trained a model.
Or wired up an API.
Or built something that looks like it works.

And then reality kicks in.

The system is slow.
It breaks in edge cases.
Costs spiral.
Users behave differently than your test data.
And suddenly, what felt like progress turns into uncertainty.

I’ve seen this pattern repeat over and over again.

Across startup founders trying to ship their first AI product.
Across engineers experimenting with LLMs and pipelines.
Across graduates who know the theory but haven’t yet built something that lives in the real world.

Everyone can build something with AI now.

Very few can make it work in production.

The Gap Nobody Talks About

Over the past few years, I’ve worked across different layers of AI systems.

From classical machine learning pipelines
to deep learning models
to modern LLM-based applications

And what became obvious very quickly is this:

The hard part is not the model.

It’s everything around it.

It’s deciding whether you even need a model.
It’s designing systems that don’t collapse under real usage.
It’s handling messy data, unreliable inputs, and unpredictable users.
It’s making trade-offs between cost, latency, accuracy, and maintainability.

And most importantly, it’s learning how to think differently.

Because building AI systems is not just coding.
It’s a different way of approaching problems.

Why I Wrote This Book

From Model to Product: Foundations of Practical AI Engineering

This book came out of that frustration.

Not frustration with AI itself,
but with how incomplete the conversation around it is.

We have tutorials on models.
We have courses on algorithms.
We have endless demos.

But when it comes to building something that actually works in the real world,
most people are left to figure it out on their own.

So I decided to write the book I wish existed when I started.

A book that doesn’t just tell you how to build a model,
but shows you how to turn that into a real system.

What Makes This Different

This is not a “learn AI in 10 days” book.

It’s not a collection of code snippets either.

Instead, it focuses on how you think.

How you frame problems before touching a model.
How you decide between rules, ML, or LLMs.
How you design systems that survive real usage.
How you reason about trade-offs instead of chasing hype.

You’ll see patterns that repeat across real systems.
You’ll understand why some architectures fail.
And more importantly, you’ll learn how to avoid those mistakes yourself.

Who This Is For

If you’re a startup founder trying to build an AI product, this will save you time, money, and probably a few failed attempts.

If you’re a software engineer, this helps you adapt. AI is not replacing engineering. It’s reshaping it.

If you’re a student or recent graduate, this gives you something most courses don’t:
a way to connect theory to reality.

And if you’ve already built AI demos but struggled to take them further,
this is exactly the bridge you’ve been missing.

A Personal Note

Every chapter in this book is shaped by real experience.

Things that worked.
Things that failed.
Things that looked right on paper but broke in production.

It’s not written from a distance.
It’s written from being in the middle of it.

And that’s intentional.

Because AI is not just about models.

It’s about building systems that people can rely on.

Get the Book

If you’re serious about moving beyond prototypes and building real AI systems, you can get the book here:

UK (direct purchase):
https://dulandias.com/product/from-model-to-product-foundations-for-practical-ai-engineering-softcover/

Rest of the world (via Amazon):
https://www.amazon.co.uk/Model-Product-Foundations-Practical-Engineering/dp/B0GTHK26RL/ref=sr_1_1

AI is moving fast.

But speed without direction leads nowhere.

This book helps you build that direction.