Abstract: When putting models into production it’s critical to know how they’re performing over time. As the last mile of the data pipeline, models can be impacted by a variety of issues, often outside the control of the data science team. “Observability” promises to help teams detect and prevent issues that could impact their models—but what is observability vs. data observability vs. ML observability? Get practical answers and recommendations from Kyle Kirwan, former product leader for Uber’s metadata tools, and founder of data observability company, Bigeye.
Bio: Kyle Kirwan wants to help the world make magic with data. He is the co-founder and CEO of Bigeye, a data reliability engineering platform that helps data teams build trust in the data their organizations depend on. As one of the first data scientists, data analysts, and product managers at Uber, he helped launch teams like Experimentation Platform, and products like Databook.
