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This means that the model needs to be registered in the Model Registry before you can use this function.
If you want to add libraries to a model without registering it, you might need to manually package the model along with its dependencies. This could involve saving the model and its dependencies to a file, and then loading them when you want to use the model. Please note that this approach would not leverage the MLflow Model Registry and its versioning capabilities.
What I don't like is that in step 2 the model is registered then in step 3, the same model (with the compiled package is registered again). So, I endup with two copies of the same model.
I ended up publishing the library to AWS CodeArtifact repository. Now, how can I tell MLFlow to use AWS CodeArtifact private repository instead of PyPi?
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