The Deployed Data Scientist: MLOps and Analytics in Practice

by Ankit Anand, Scott Burk, Kinshuk Dutta

268 pages, Kindle Edition

Published June 3, 2026

Reviews

[Timon]

The Deployed Data Scientist: MLOps and Analytics in Practice is an excellent practical guide for anyone who wants to understand what it really takes to move machine learning from experimentation into reliable production systems. What stood out to me most is its emphasis on the fact that successful ML is about far more than building a good model—it also requires strong data pipelines, deployment practices, monitoring, governance, and clear business ownership.

I particularly appreciated the book’s coverage of the full MLOps lifecycle, along with timely topics such as data drift, model observability, CI/CD, cloud infrastructure, LLMOps, RAG, responsible AI, and human in the loop systems. The material feels highly relevant to the challenges data teams face today.

Clear, practical, and thoughtfully structured, this is a valuable resource for data scientists, ML engineers, analytics professionals, and leaders working to build dependable AI systems.