MLOps Salon:Applying MLOps at Scale - removing the need to write model deployment code at Stitch Fix

Presented by: Stefan Krawczyk - Manager & Lead ML Platform Engineer at Stitch Fix.
In this talk, Stefan Krawczyk covers how the Model Lifecycle team on Data Platform built a system dubbed the “Model Envelope” to enable “deployment for free”. That is, no code needs to be written by a data scientist to deploy any python model to production, where production means either a micro-service, or a batch python/spark job. With our approach we can remove the need for data scientists to have to worry about python dependencies, or instrumenting model monitoring since we can take care of it for them, in addition to other MLOps concerns. Specifically the talk will cover: *Our API interface we provide to data scientists and how it decouples deployment concerns. *How we approach automatically inferring a type safe API for models of any shape. *How we handle python dependencies so Data Scientists don’t have to. *How our relationship & approach enables us to inject & change MLOps approaches without having to coordinate much with Data Scientists.