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.
Isabel Zimmerman is a software engineer on the open source team at RStudio, where she works on building MLOps frameworks. When she's not geeking out over new data science techniques, she can be found hanging out with her dog or watching Marvel movies.
