While data scientists have an intuition of what goes into training a machine learning model, 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. This talk will highlight different ways that models can be put into production, and where each deployment is best (and worst!) suited.
By the end of this talk, people will understand what the term MLOps entails, different options for deployment, and when different methods work best. Listeners will also walk away with practical knowledge on how to use the MLOps framework
vetiver to version, share, deploy, and monitor models in Python and R.