Quick answer: OSS ML lifecycle platform — experiment tracking, model registry, deployment.
MLflow is an open-source ML lifecycle management platform built by Databricks. It provides a unified environment for tracking experiments, packaging models, and deploying machine learning applications across multiple frameworks and environments. MLflow addresses the complexity of managing ML workflows by offering four core components: Tracking (logging parameters, metrics, and artifacts), Projects (reproducible packaging of code), Models (standardized format for different frameworks), and Registry (centralized model storage with versioning). It integrates seamlessly with popular ML frameworks like TensorFlow, PyTorch, and scikit-learn, making it a neutral choice for teams using diverse toolstacks. As an open-source tool, MLflow is free to deploy on-premises or in your own cloud infrastructure.