MLOps with MLflow: Hyperparameter Tuning ML Models

“Hyperparameter tuning an essential step to improve model performance involves modifying a model s parameters to find the best combination for optimal results. The integration of MLflow with Databricks unlocks a powerful combination that enhances the machine learning (ML) workflow.

First you will explore the collaborative potential between MLflow and Databricks for machine learning projects. You will learn to create an Azure Databricks workspace and run MLflow models using notebooks in Databricks establishing a robust foundation for model development in a scalable environment. Additionally you will set up Databricks File System (DBFS) as a source of model input files.

Next you will implement hyperparameter tuning using MLflow and its integration with the hyperopt library. You will define the objective function search space and algorithm to optimize model performance. Through systematic tracking and comparison of hyperparameter configurations with MLflow you will find the best-performing model setups.

Finally you will integrate SQLite with MLflow allowing efficient management and storage of experiment-run data. You will create a regression model using scikit-learn and statsmodels comparing the processes for the two.”