Data Science In Production

Getting a machine learning model to perform well on a laptop is one thing, but making it work reliably for thousands of users is an entirely different challenge. That gap between a promising prototype and a robust, live system is where Data Science In Production lives. This podcast digs into the messy, practical realities that data scientists and ML engineers face every day. You'll hear candid conversations about the specific tools and techniques that actually work under pressure, not just in theory. The focus is squarely on the people and processes that turn code into a valuable service, covering everything from deployment pipelines and monitoring to managing stakeholder expectations and team dynamics. Each episode aims to provide actionable insights that can shorten the journey from a clean notebook to a model that delivers real-world impact. Tune in for a straightforward, no-fluff look at the engineering discipline required to build and maintain machine learning in production.