Introduction
Hyperparameters are parameters defined before model training that can influence the model's performance. There are different hyperparameters available to fine-tune depending on the algorithm used to train a model, which can be done through a process called hyperparameter tuning.
In this module, you'll learn how to use Azure Databricks with MLflow to do hyperparameter tuning and model selection.
Learning objectives
After completing this module, you’ll be able to:
- Understand hyperparameter tuning and its role in machine learning.
- Learn how to use the two open-source tools - automated MLflow and Hyperopt - to automate the process of model selection and hyperparameter tuning.