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I have complete pipeline in place from data inputs to final predictions. I have 10 models in place (wrote function to automate the whole process) then , Choose 1 model based on accuracy. Now, I need to store all the model(because any model can have better accuracy as data changes) and reuse it with new values of inputs from my train features.

As all process is very dynamic, for each step job trigger and does the job. Need help in how I can save and reuse this model which aligned with this flow.

Thank you in advance. an example would be a great help

Amit Kumar
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did you try ?

mlflow.pyfunc.log_model  for saving the model
mlflow.pyfunc.load_model(f"models:/{model_name}/production") for loading the model

mlflow.pyfunc

binar
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  • Your answer could be improved with additional supporting information. Please [edit] to add further details, such as citations or documentation, so that others can confirm that your answer is correct. You can find more information on how to write good answers [in the help center](/help/how-to-answer). – Community Jan 07 '22 at 10:43
  • @binar I tried in using pickle. But I am not able to see where it got stored. This solution can help me for a single model but I have multiple model in place I need to save it at go and wrap it into a function. Can you help in that ? – Amit Kumar Jan 07 '22 at 18:08