// The Book
Modern Analytics Engineering
The Data Practitioner’s Guide to AI-Ready Systems
From framing business problems to deploying production ML: data acquisition, feature engineering, statistical validation, deep learning, NLP, generative AI, RAG, and agentic AI, with the engineering discipline that keeps them reliable at scale.
- Build reproducible, high-trust datasets and data contracts
- Move from exploratory notebooks to service-oriented engineering
- Ship and monitor production ML, feature stores, validation, rollback
- Reason about LLMs, RAG, and agentic AI with human oversight built in
// Why this book
80% of AI projects fail on weak foundations
This book gives data practitioners the engineering discipline to ship AI that survives production, organized around the four-stage Production-Grade AI maturity model.
Foundations
Data contracts, lineage, and AI-ready storage, the substrate models depend on.
Pipelines
Reproducible, tested feature & model pipelines that move from notebook to repo.
Deployment
Models in production behind monitoring, validation, and rollback.
Governance
Continuous validation, risk controls, and audit trails that keep systems trustworthy at scale.
// Inside
What you’ll learn
- Build reproducible, high-trust datasets and data contracts
- Move from exploratory notebooks to service-oriented engineering
- Ship and monitor production ML, feature stores, validation, rollback
- Reason about LLMs, RAG, and agentic AI with human oversight built in
Who it’s for
For data scientists, ML engineers, analytics engineers, and technical leaders.
Format
Hardcover / paperback & Kindle · ISBN 9798999920881
// Get the book
The discipline that gets AI into production.
From foundations to agentic systems, the full method in one volume.