Using MLFlow for Tracking

Now that you and your team have ported your previous machine-learning processes into the Databricks environment, you are about to start a new machine-learning project.

You are tasked with developing a new recommendation engine that takes in context from previous book reviews. Since you are developing a new model, you are still determining exactly what framework or parameters will result in the best model. This would be a great opportunity to use MLFlow to track all of your model runs, and then you can pick the best model from there.