In this guide, we venture into a frequent use case of MLflow Tracking: hyperparameter tuning.
Our journey will begin with a detailed notebook that showcases hyperparameter tuning using Optuna, and how each of these tuning runs are logged seamlessly with MLflow. Following this, we’ll delve deeper, exploring alternative APIs and techniques that can be leveraged to further enhance our model tracking capabilities.
Throughout this guide, our focal points will be:
Introducing the capabilities of MLflow for tracking hyperparameter tuning
Understanding the distinction between parent and child runs in MLflow
Delving into the components of MLflow and their relevance in our workflow
Discovering and navigating experiments, parent runs, and child runs in the MLflow UI
Conducting a comparative analysis of runs to understand the nuances of hyperparameter tuning
To get started with this guide, click NEXT below.