An Introduction to ML Ops  Building data science products requires many things we’ve discussed on this podcast before: insight, customer empathy, strategic thinking, flexibility, and a whole lot of determination. But it requires one more thing we haven’t talked about nearly as much: a stable, performant, and easy-to-use foundation. Setting up that foundation is the chief goal of the field of machine learning operations, aka ML Ops. This month on the Klaviyo Data Science Podcast, we give a brief but thorough introduction to the field of ML Ops. You’ll hear about: How ML Ops is different from the similar fields of data science and DevOps What skills a successful ML Ops developer should have, and what an ML Ops developer’s day-to-day looks like Why concepts like “velocity” and “stability” have their own special nuances in the world of ML Ops For the full show notes, including who's who, see the ⁠⁠⁠⁠⁠⁠Medium writeup⁠⁠⁠⁠⁠⁠.