Ankit Anand
Technology leader, researcher, and Managing Consultant focused on data quality, master data management, and reliable enterprise AI.
Author profile →A practical guide to production machine learning
The Deployed Data Scientist
MLOps and Analytics in Practice
The Deployed Data Scientist is a practical guide to deploying, operating, monitoring, and scaling machine learning and analytics systems in production. It connects data strategy, model engineering, CI/CD, cloud infrastructure, observability, LLMOps, and business ownership into one operating model for dependable AI.
Bibliographic record
Book Details
- Full title
- The Deployed Data Scientist: MLOps and Analytics in Practice
- Authors
- Ankit Anand, Dr. Scott Burk, and Kinshuk Dutta
- Publisher
- Technics Publications
- Publication date
- ISBN-10
- Not assigned; this edition uses a 979 ISBN-13
- ISBN-13
- 979-8898160982
- Edition
- First edition
- Formats
- Paperback and Kindle edition
- Page count
- 209 pages
- Language
- English
- Official publisher
- Technics Publications
- Retailers
- Amazon · Barnes & Noble
Author relationship
The Authors
Read the roadmap
Chapter Guide
Each chapter page offers original context, practical questions, and a direct path back to the canonical book record.
01The Mindset ShiftFrom notebooks to data products
02Data StrategyData contracts and model decay
03Production-Ready ModelsSelection, evaluation, and registries
04Automated Assembly LineML testing and CI/CD
05Cloud InfrastructureArchitecting for scale
06Monitoring and ObservabilityFinding failure before users do
07Advanced DeploymentXAI, HITL, and edge systems
08Generative AI and LLMOpsOperating the LLM lifecycle
09Data Leader's PlaybookTeams, platforms, and economics
Reader questions
Frequently Asked Questions
What is The Deployed Data Scientist about?
It is a practical guide to turning machine learning experiments into dependable production systems, covering data, models, deployment, monitoring, governance, and business ownership.
Who is the book for?
It is written for data scientists, ML engineers, data leaders, analytics professionals, architects, and anyone responsible for the outcomes of an AI system.
Is it suitable for data scientists and ML engineers?
Yes. Data scientists will find a path beyond the notebook, while ML engineers will find a systems view spanning pipelines, registries, CI/CD, cloud infrastructure, and operations.
What MLOps topics does the book cover?
It covers data strategy, data contracts, model selection, evaluation, registries, containerization, testing, continuous integration, continuous delivery, cloud architecture, observability, retraining, and governance.
Does it cover LLMOps and Generative AI?
Yes. Chapter 8 covers the LLM lifecycle, prompt engineering, Retrieval-Augmented Generation, hallucination monitoring, cost, latency, and production LLM operations.
Does it cover model monitoring, CI/CD, and data contracts?
Yes. Model monitoring and observability are central to Chapter 6, CI/CD is developed in Chapter 4, and data contracts are introduced as a foundation in Chapters 1 and 2.
How does it differ from traditional data science books?
It focuses on what happens after the model works: reliable data, deployment, feedback loops, monitoring, accountability, and the operating choices that make AI useful to a business.
What experience should a reader have?
No advanced specialization is required. Familiarity with data science, software delivery, or analytics helps, but the book is designed to give readers the shared language needed across those disciplines.
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Use the ISBN-13 979-8898160982 when comparing catalogs and editions.