Data scientists have an intuition of what goes into training a machine learning model, but building an MLOps strategy to deploy that model can sound daunting for data science teams. Model services are not one-size-fits-all, so it is imperative to know a range of tools available. One option, Vetiver, is a framework for R and Python created to make model deployment feel like a natural extension of a data scientist’s skill set.
This talk offers a high-level overview of what MLOps options are available for model operationalization, but also shows a practical example of an end-to-end MLOps deployment of a model-aware REST API using Vetiver.