Modern Analytics Engineering: The Data Practitioner's Guide to AI-Ready Systems

Modern Analytics Engineering guides data practitioners from analytical foundations to production AI systems. The book addresses a critical gap: professionals who can build analytically sound work in notebooks but struggle to deploy it reliably, scale it sustainably, or reason about intelligent systems in production. Across 14 chapters and 3 bridge modules, readers develop both the statistical rigor and engineering mindset required for systems that organizations depend on.Part 1 covers analytics foundations including problem framing, causal reasoning, feature engineering, and statistical validation. The Bridge modules transform practitioners from exploratory coders to service oriented engineers through pipeline architecture, data contracts, and MLOps infrastructure. Part 2 addresses intelligence systems: machine learning, deep learning, NLP, large language models, retrieval augmented generation, and agentic AI with human oversight.Written for data scientists, ML engineers, analytics engineers, and technical leaders, this book provides the frameworks to build systems that run reliably at scale while developing the judgment to deploy AI responsibly. Read more