Book of the Month: The Deployed Data Scientist 

For July, the Book of the Month is The Deployed Data Scientist by Ankit Anand, Scott Burk, and Kinshuk Dutta. With all the AI talk over the last couple years, it’s nice to be reminded of the real work done by data scientists deploying machine learning and achieving results for their organization. 

This book is technical. The team that put this together pulls no punches, but if you’ve got a coding background, you’ll be able to pickup this book and turn yourself into a data scientist. The book itself will often give the reader large chunks of code in python, SQL, and other languages. It doesn’t just throw code at the reader though, it explains everything along the way and does a lot to teach the reader what’s going on. This helps describe the complexity of what’s in an S3 bucket somewhere, a detailed deployment option, or a YAML file on your way to describing a data set for a model.  

Right out of the gate, the authors tell a tale of a data model that drifts away, with a data model identifying customers at risk of churn when they were indeed loyal customers to an organization. They kick off the story with “a tale of how much caffeine the human body can absorb” and go from there. Even though the book is rather technical, this grounding in the reality from the perspective of people doing the work makes this book an excellent read, and this type of realism floods the entire book. Even later – when talking about containerization and deploying Kubernetes – the book always reads like a friend giving you their advice to help you along the way. 

Each chapter in the book ends with a chapter summary, which is also a great way to anchor the reader to the concepts the book is laying out. Sometimes the authors add a little flair in the summary, for example “Accuracy is a Vanity Metric,” a statement which will give many a reader pause. The authors are doing a callback to earlier in the chapter when they discuss how the simplicity of accuracy alone as a metric can hide the performance of a model, so they give some alternatives to provide more value instead of something as banal as “accuracy.” 

There’s a lot in this book to enjoy for a team deploying a data science and machine learning solution. The book also touches on key planks like strategy and governance, critical to the success of any initiative.  Overall, this will be a helpful tome to any team that is starting to get into data science, or a team that is actively maintaining models, or changing the architecture of existing models. The authors also put this together in a way that teaches machine learning operations to anyone with a development mindset, giving the reader new marketable skills they can bring to an organization. 

More About the Authors 

Dr. Scott Burk is the founder of It’s All Analytics. He is the author of eight books on AI, data science, and analytics, including the It’s All Analytics Series. He currently teaches in the MS in Data Science program at CUNY and has taught and developed curriculum at Baylor, Texas A&M, and SMU. He has solved complex AI, statistical, and analytical problems at Dell, Texas Instruments, PayPal, eBay, Overstock.com, healthcare organizations, energy firms, semiconductor and manufacturing companies, startups, and many others worldwide. Scott holds a B.S. in biology and chemistry, M.S. degrees in finance, statistics, and data mining, and a Ph.D. in statistics. Data has been the unifying thread across his professional experience.

Kinshuk Dutta is a technology leader with nearly two decades of experience driving innovation in Data, Integration & Cyber Security. He is the co-author of the recent best-selling Data for AI, founder of  Be Cognizant of Data (datanizant.com) – where he advocates for responsible data practices and AI enablement – and a named inventor on an AI patent. Kinshuk is a senior member of IEEE, recognized for shaping the future of intelligent systems and guiding organization through AI-driven transformation.

Ankit Anand is a strategic data leader with over 17 years of experience in designing and implementing enterprise-scale data solutions. He has led global initiatives, including at Koch and Lennox Industries, focusing on data management, architecture, and modern data stack implementation, and is a senior member of IEEE with pending patents in data management and agentic AI.