MLflow is an open-source platform designed to streamline machine learning development. It facilitates tracking experiments, packaging code into reproducible runs, and deploying models. Key components include MLflow Tracking for logging and comparing experiments, MLflow Projects for sharing code, MLflow Models for deploying models, and the MLflow Model Registry for managing model lifecycles. It supports various ML libraries and can be integrated into local and cloud environments.
Table of contents
InstallingDocumentationRoadmapCommunityRunning a Sample App With the Tracking APILaunching the Tracking UIRunning a Project from a URISaving and Serving ModelsOfficial MLflow Docker ImageContributingCore MembersSort: