MLflow

MLflow is an open-source platform for managing the end-to-end machine learning lifecycle, covering experiment tracking, reproducible runs, model packaging, and deployment.

beginner30 minutes4 steps

The play

Install MLflow
Install MLflow using pip. This will provide the core MLflow functionalities.
Track an Experiment
Track a simple experiment by logging parameters and metrics. This example simulates training a model and logging the training parameters and the resulting accuracy.
Run the MLflow UI
Start the MLflow UI to view the tracked experiment. This command will launch a local server where you can explore your experiments.
Log a Model
Log a trained model to MLflow. This example uses scikit-learn to train a simple linear regression model and then logs it to MLflow.

Starter code

Start by installing MLflow and then tracking a simple experiment to understand the basic workflow.

Source

Articlemlflow.org