MLflow is an open-source platform designed to streamline the machine learning lifecycle, offering tools for experiment tracking, model packaging, deployment, and model registry management. It enables data scientists and engineers to collaborate efficiently and deploy models seamlessly across various environments.

Features of MLflow:

MLflow Tracking allows logging and querying of experiments, capturing parameters, metrics, and artifacts to ensure reproducibility.
MLflow Projects provide a structured format to package machine learning code, facilitating collaboration and reproducibility.
MLflow Models standardize the way ML models are packaged and deployed across multiple frameworks, making model serving easier.
MLflow Model Registry offers a centralized repository for model versioning, staging, and deployment, ensuring smooth transitions from development to production.

To check MLflow version:

$ mlflow --version

Disclaimer: MLflow is an open-source machine learning lifecycle platform released under the Apache License 2.0. It is not associated with, endorsed by, or sponsored by any specific company. MLflow is provided "as is," without any warranty, express or implied. Users utilize this software at their own risk. The developers and contributors to MLflow disclaim any responsibility for any damages, losses, or consequences arising from the use of this software. Users are encouraged to review and adhere to licensing terms and any relevant regulations while utilizing MLflow.

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