MLflow is an open-source platform for managing the complete machine learning lifecycle, from experimentation to deployment. It simplifies the process of tracking experiments, packaging code into reproducible projects, and managing and serving models in production environments, all within a unified framework.

Features of MLflow:

• Tracking experiments, including metrics, parameters, and artifacts.
• Packaging machine learning code into reusable projects for reproducibility.
• Managing and versioning machine learning models with the Model Registry.
• Serving machine learning models via a REST API with MLflow Serving.
• Integrates with popular machine learning libraries such as TensorFlow, PyTorch, and Scikit-learn.

To check MLflow version: python -c "import mlflow; print(mlflow.__version__)"

Disclaimer: MLflow is an independent open-source project maintained by Databricks and the community. It is not affiliated with, endorsed by, or sponsored by Apache Spark, TensorFlow, or other machine learning libraries. It is provided "as is," without any warranty, express or implied. Users are responsible for understanding and complying with licensing terms, data policies, and regulations while deploying MLflow.