MLOps Methods 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 fact for big firms is that each one the steps are taking place at one time in live performance with different fashions, with shifting information, and, generally, misaligned key function inputs.

Furthermore, regulated companies are required to trace all of the fashions, the modifications, and the impacts of these modifications for compliance. Enter explainability supported by mannequin monitoring. Removed from the outdated, single activity strategy of solely monitoring modifications and anomalies, as we speak’s ML monitoring does way more. It makes use of AI to ship full featured efficiency administration that may establish modifications, alert the suitable individuals, and pop fashions again into manufacturing in actual time with correct governance.

FiddlerAI is a startup centered on enterprise mannequin efficiency administration. They’re tackling the distinctive challenges of constructing in-house steady and safe MLOps techniques at scale. In the present day we’re interviewing Krishna Gade about trusting AI, the technical challenges of ML monitoring and the actual world downside statements past compliance that explainability can deal with.

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