Myles Mitchell continues a series on Vetiver:
In those blogs, we introduced the {vetiver} package and its use as a tool for streamlined MLOps. Using the {palmerpenguins} dataset as an example, we outlined the steps of training a model using {tidymodels} then converting this into a {vetiver} model. We then demonstrated the steps of versioning our trained model and deploying it into production.
Getting your first model into production is great! But it’s really only the beginning, as you will now have to carefully monitor it over time to ensure that it continues to perform as expected on the latest data. Thankfully, {vetiver} comes with a suite of functions for this exact purpose!
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