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I am a big fan boy of tidymodels and played around with vetiver + pins in R and Python in order to not only develop models but actually deploy them.

However, if you are looking for tools that support in the area of MLOps, sooner or later you will stumble across MLflow. Just like vetiver + pins, MLflow helps to track and deploy models and to build a model registry. I see some pros for vetiver like:

  • you can directly dockerize a model or create a REST service
  • vetiver+ pins is very, very easy to use and does not require a lot of setup

At this point, I'd like to ask the community if there are any other advantages of vetiver + pins over MLflow or is it advisable to use MLflow directly, since it is completely agnostic regarding the programming language and already has a very large community? Many thanks for your answers! M.

Mischa
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    The integration of MLFlow comes earlier on in your data science processing, with emphasis on experiment tracking/model registries. For things like deploying, it becomes necessary to use CLI. Vetiver comes later in a workflow, so it focuses more on lightweight versioning and being able to quickly deploy models. There is more support in the Python/R API to do things such as make predictions/interacting with your model without using CLI. You could also use a combination of both, it all depends on your use case! – Isabel Zimmerman Sep 06 '22 at 13:57

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