Deployed data scientist
Deployed Data Scientist Author
MELISSA, Texas, July 5, 2026 /EINPresswire.com/ — The Deployed Data Scientist: MLOps and Analytics in Practice, co-authored by Ankit Anand, Dr. Scott Burk, and Kinshuk Dutta, helps data professionals address one of the industry’s most pressing challenges by focusing on the successful deployment, management, and long-term operation of machine learning systems. Published by Technics Publications, this book provides practical guidance for data scientists, machine learning engineers, analytics leaders, and technology professionals who want to move their AI initiatives beyond experimentation and into trusted production environments.
This publication explores the entire machine learning operations (MLOps) lifecycle, covering data strategy, model engineering, CI/CD pipelines, cloud infrastructure, observability, governance, and emerging practices in generative AI and LLMOps. Through real-world scenarios and implementation-focused guidance, the authors present a structured framework for creating AI systems that continue to provide value after deployment.
This book is available in paperback on Amazon worldwide. To learn more about The Deployed Data Scientist or to purchase the book, visit https://technicspub.com/the-deployed-data-scientist/.
A practical guide for modern data science teams
Many organizations have invested heavily in artificial intelligence but face challenges in operationalizing machine learning models at scale. Data scientists in place will address these practical realities by examining the systems, processes, and governance required to maintain reliable AI solutions over time.
Readers will gain insight into topics such as data contracts, model registries, automated testing, monitoring model drift, cloud deployment strategies, explainable AI, and responsible AI governance. The book also explains how organizations can prepare for modern enterprise applications, including large-scale language models, while maintaining operational discipline and accountability.
Bridging technical execution and business value
Rather than viewing deployment as the final step in model development, this book encourages professionals to treat machine learning systems as long-term business products that require continuous monitoring, maintenance, and improvement.
The authors demonstrate how data science teams can enhance collaboration across engineering, analytics, governance, and business leadership by connecting technical implementation with organizational goals. This perspective helps readers understand how production-ready AI contributes to measurable operational outcomes and sustainable enterprise adoption.
Supporting experts throughout the AI lifecycle
Designed for both experienced practitioners and professionals venturing into production AI, this book combines technical depth with easy-to-understand explanations and practical examples. The scope ranges from basic MLOps concepts to advanced topics such as search augmented generation (RAG), human-in-the-loop frameworks, model observability, and enterprise AI architecture. Here is a recently published article about this book.
“Our goal was to provide practical resources to help professionals understand what it takes to build AI systems that remain reliable, scalable, and valuable after deployment,” said Ankit Anand, co-author of The Deployed Data Scientist: MLOps and Analytics in Practice. “AI success depends on disciplined operations, thoughtful governance, and a commitment to continuous improvement throughout the lifecycle of any model.”
About Technics Publications
Technics Publications is an independent publisher specializing in specialized resources in technology, analytics, artificial intelligence, cybersecurity, software development, and data science. The company publishes practical books that support professionals, educators, and organizations seeking the latest, real-world technical knowledge across rapidly evolving industries.
Ankit Anand, Scott Burke, Kinshuk Dutta
Author of The Deployed Data Scientist
manaakit@gmail.com
Legal disclaimer:
EIN Presswire provides this news content “as is” without warranty of any kind. We do not accept any responsibility or liability for the accuracy, content, images, videos, licenses, completeness, legality, or reliability of the information contained in this article. If you have any complaints or copyright issues related to this article, kindly contact the author above.
