Experiment Tracking using MLFlow in Machine Learning

Experiment tracking is the process of recording all the important components such as hyper parameters, metrics, models and artifacts like plots PNG images, files etc. Experiment tracking helps to reproduce the old results by using the stored parameters. Under one experiment different runs can be created and by changing the parameters value we can evaluate model performance. And easily do the model performance comparison and finalize the optimal model for production. MLFlow is the widely used tool for experiment tracking across organizations.

MLFlow Components — Source: https://www.mlflow.org/

To explain, how experiment tracking works and how to implement it using python, I have created a video with below points. [Notebook code]

Create conda Environment
Train a Basic Machine Learning classifier using Random Forest
Create experiment with basic classifier and records metrics
Fine tune the model using hyper parameter tuning random search CV method
Create another experiment and log all hyper parameters, metrics, and artifacts which contains model, roc_auc curve PNG, confusion Matrix PNG.