MLflow

MLflow is a powerful open-source machine learning lifecycle platform designed to manage experiments, track metrics, store models, and organize artifacts. It helps data scientists and DevOps teams streamline model development, evaluation, and deployment through a centralized web interface.

Key Features of MLflow:

• Open-source platform for managing the ML lifecycle.
• Experiment tracking with metrics, parameters, and artifacts.
• Model packaging and version management.
• Web-based UI for monitoring runs.
• Easy integration with TensorFlow, scikit-learn, and other ML frameworks.

MLflow Usage:

$ sudo su
$ python3 -m venv mlflow-env
$ source mlflow-env/bin/activate
$ mlflow --version
$ mkdir -p ~/mlruns
$ mlflow ui --host 0.0.0.0 --port 5000 --allowed-hosts '*'

To Access Web Interface:
http://:5000

To Stop MLflow:
Press CTRL + C in the terminal running MLflow.

Disclaimer:
MLflow is an open-source machine learning platform maintained by its developer community. This content is provided for informational purposes only. We are not affiliated with or endorsed by MLflow. All trademarks and product names belong to their respective owners.