Although we like to think about ML workflows as straight-line narratives from experiment to training to production, and then finally monitoring; the reality for large companies is that all the steps are happening at one time in concert with other models, with shifting data, and, sometimes, misaligned key feature inputs. Moreover, regulated firms are required to track all the models, the changes, and the impacts of those changes for compliance. Enter explainability supported by model monitoring. Far from the old, single task process of only monitoring changes and anomalies, today’s ML monitoring does much more. It uses AI to deliver full featured performance management that can identify changes, alert the right people, and pop models back into production in real time with proper governance. FiddlerAI is a startup focused on enterprise model performance management. They are tackling the unique challenges of building in-house stable and secure MLOps systems at scale. Today we are interviewing Krishna Gade about trusting AI, the technical challenges of ML monitoring and the real world problem statements beyond compliance that explainability can address. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post MLOps Systems at Scale with Krishna Gade appeared first on Software Engineering Daily.