MLOps Systems at Scale with Krishna Gade
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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.
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