Model Maintenance & Tracking with MLFlow
In this class I explain the basics of the package MLFlow. MLFlow is by far my favorite package for tracking and maintaining machine learning models during training and in production. It is simple and easy to use and brings great value particularly in the model training process. It is available in multiple languages inside a Spark platform or even with regular Python or R on your local computer.
I cover the basics of how to track models with MLFlow including model hyperparameters, evaluation metrics, and logging models to reproduce runs. I then cover how to train multiple models in a production scenario and elevate the best model to production to be scheduled.
To gain access to code, data, and course materials visit https://kelseyemnett.com/2021/07/24/m....
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