MLOps Techniques at Scale with Krishna Gade

 Though we like to consider ML workflows as straight-line narratives from experiment to coaching to manufacturing, after which lastly monitoring; the truth for big firms is that every one the steps are taking place at one time in live performance with different fashions, with shifting information, and, generally, misaligned key characteristic inputs.

Furthermore, regulated corporations are required to trace all of the fashions, the adjustments, and the impacts of these adjustments for compliance. Enter explainability supported by mannequin monitoring. Removed from the previous, single process means of solely monitoring adjustments and anomalies, right now’s ML monitoring does far more. It makes use of AI to ship full featured efficiency administration that may establish adjustments, alert the precise individuals, and pop fashions again into manufacturing in actual time with correct governance.

FiddlerAI is a startup targeted on enterprise mannequin efficiency administration. They’re tackling the distinctive challenges of constructing in-house secure and safe MLOps methods at scale. At the moment we’re interviewing Krishna Gade about trusting AI, the technical challenges of ML monitoring and the true world drawback statements past compliance that explainability can tackle.


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