Tracking model parameters
As we have already seen, there are lots of benefits of using auto-logging in MLflow, but if we want to track additional model parameters, we can either use MLflow to log additional parameters on top of what auto-logging records, or directly use MLflow to log all the parameters we want without using auto-logging at all.
Let's walk through a notebook without using MLflow auto-logging. If we want to have full control of what parameters will be logged by MLflow, we can use two APIs: mlflow.log_param and mlflow.log_params. The first one logs a single pair of key-value parameters, while the second logs an entire dictionary of key-value parameters. So, what kind of parameters might we be interested in tracking? The following answers this:
- Model hyperparameters: Hyperparameters are defined before the learning process begins, which means they control how the learning process learns. These parameters can be turned and can directly affect how well...
