Published · Available Now

Applied AI
& MLOps

// Building Production-Grade AI Systems That Actually Ship

The gap between a brilliant model in a notebook and a system that survives production is where most AI projects die. This book closes that gap — chapter by chapter, tool by tool.

13 Chapters Production-Grade ML Engineers Data Scientists MLOps · DevOps · LLMs

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What You'll Learn

From Notebook to Production.
No Shortcuts.

⚙️

Build Real Pipelines

Design and deploy scalable ML pipelines with Airflow, dbt, Spark, and Kafka — systems that run in production, not just on your laptop.

🚀

Deploy with Confidence

CI/CD for ML, Docker containerisation, cloud deployment on AWS/GCP/Azure, and reproducibility that survives team handoffs.

🔍

Monitor & Maintain

Catch model drift before it costs you. Build observability, alerting, and rollback systems — because a model you can't monitor is a model you can't trust.

🧠

NLP, CV & LLMs in Production

Deploy NLP pipelines, computer vision systems, and LLMs — including RAG architectures, transformer optimisation, and responsible inference at scale.

🛡️

Ethics & Compliance

Bias detection, fairness audits, explainability, and navigating GDPR and AI regulation — built into your pipeline, not bolted on after.

💡

Real-World Case Studies

Finance, healthcare, and retail failures turned into lessons. The $15M model drift story. The $12M accuracy trap. Learn from production disasters without living them.

Contents

13 Chapters. Every Layer of the Stack.

Ch 01

Introduction to Real-World AI

AI in industry vs academia — what it really means for a model to succeed.

Ch 02

Data Collection & Preprocessing

Turning messy reality into machine-ready data. Versioning. Validation. The $500K timezone mistake.

Ch 03

Supervised & Unsupervised Learning

Training models that deliver impact. Why 95% accuracy still cost one bank $12M.

Ch 04

Deep Learning with TensorFlow & PyTorch

Production-ready neural networks. Optimisation, efficiency, and real-world constraints.

Ch 05

Scalable ML Pipelines & Automation

From Jupyter notebooks to production-grade workflows with Airflow, dbt, and Python.

Ch 06

Containerisation & Reproducibility

Docker, MLflow — guarantee your ML works everywhere, every time.

Ch 07

CI/CD for Machine Learning

Automate everything from code commit to model deployment. Reliable, repeatable workflows.

Ch 08

Cloud Deployment of ML Systems

AWS, GCP, Azure — scale your models to the world without burning your budget.

Ch 09

Monitoring & Observability

Detect drift, prevent failures, prove model trustworthiness. The $15M silence problem.

Ch 10

NLP in Production

End-to-end NLP pipelines that thrive under real-world constraints. Latency. Tokenisation. Drift.

Ch 11

Computer Vision in Production

From pixels to production. CI/CD for CV, interpretability, containerised scalability.

Ch 12

Leveraging LLMs in Production

GPT, BERT, LLaMA — RAG, transformer architecture, moving beyond the demo.

Ch 13

Ethics, Bias & Regulation in AI

Fairness audits, explainability, GDPR, and the regulatory landscape — built in, not bolted on.

From Chapter 1

Introduction to Real-World AI

// Chapter 1 — AI in Industry vs Academia

In academic circles, artificial intelligence is about pushing boundaries, refining algorithms, squeezing out marginal accuracy gains, and crafting elegant solutions to well-defined problems. But in the real world, success means something different.

A model that can't survive noisy data, scale under load, or integrate into existing systems is just research. In production, AI must be robust, explainable, and results-driven — and above all, useful. While a paper may aim to improve accuracy by 0.1%, a company seeks solutions that reduce costs, accelerate decisions, or prevent $15 million in silent model drift.

This book is for the doers — engineers, architects, analysts, and tech leaders who want to move beyond theory and start solving real-world problems. No fluff. No vendor hype. Just sharp, useful, real-world knowledge you can apply from day one.

Audience

Who Should Read This Book

👩‍💻

ML Engineers

You've trained models. Now you need to ship them, monitor them, and keep them honest in production. This book is the missing manual.

📊

Data Scientists Moving to Prod

Your Jupyter notebooks are brilliant. This book gives you the engineering discipline to turn them into systems that run at 3am without you watching.

🏗️

AI/ML Architects & Tech Leads

You're making decisions that affect teams and systems. This book gives you the vocabulary, patterns, and trade-offs to make them well.

Seidu Ramadhan Hussein

// Machine Learning Engineer · Founder, AIforGhana · Big Data Evangelist

I've written this book not just as a data scientist — but as a builder. Over the past decade, I've worked on AI and big data systems that power national-scale platforms, fraud detection engines, and health tech diagnostics. I've seen what works — and more importantly, what breaks in production at 3am when nobody is watching. This book distills those lessons. It's the book I wish I had when I started.

MSc Data Science AWS Certified 9 Live Systems AIforGhana Founder

Applied AI& MLOps

From Notebook to Production.No Shortcuts.