Machine Learning and Data Ops in Production

Gilena samulova, sito and co founder of a i so that's a start up developing open source tools to analyze and monitor a the performance of machine learning models. Elena really brought us into her expertise in looking at what are the things that go wrong with models. How do we have observabability of models in production? How do we observe drift? And when they do inevitably go wrong, how do we bring them back on track? What are those actions? So kind of adding to the field of davops, data ops and m l ops, model ops is really its own production orient in all by itself. A teshanka dos was the next episode