Ben Wilson is a Practice Lead Resident Solutions Architect at Databricks, based in North Carolina, USA. Over the past 12 years he has worked in data science across a wide range of industries, from semiconductor manufacturing to fashion, which has given him a grounded view of what it actually takes to move machine learning out of notebooks and into production systems that survive real users.

At Databricks he helps organizations design machine learning solutions that prioritize maintainability over novelty. In practice that means refactoring monolithic code into modular, testable components, running timeboxed experiments and bake-offs, choosing simpler SQL or statistical approaches before reaching for deep learning, and building in explainability so model behavior can be translated into business terms. He often points out that production failures come less from weak algorithms and more from needless complexity and missing business buy-in.

Ben is the author of Machine Learning Engineering in Action, published by Manning, a book focused on how to get ML projects into production and help them stay there. Drawing on experience across manufacturing, retail, and technology, he shares practical guidance on testing, deployment, and collaboration that helps data scientists grow into effective machine learning engineers.