[{"data":1,"prerenderedAt":12},["ShallowReactive",2],{"stack-tr-en-mlflow":3},{"tagline":4,"description":5,"strengths":6,"useCase":11},"The open-source MLOps platform that tracks, versions and ships your models.","MLflow logs every experiment (parameters, metrics, artifacts), centralizes models in a versioned registry with aliases and tags, then packages them for serving, with evaluation and tracing for LLM applications. Open source, framework-agnostic, managed on Databricks and available on the major clouds, it's our choice to get ML out of the notebook and make it reproducible and auditable.",[7,8,9,10],"Tracking: parameters, metrics and artifacts of every run, side by side.","Model Registry: versions, aliases and tags, controlled promotion to prod.","Standard packaging served as a REST API, Docker image or cloud endpoint.","Evaluation and LLM tracing: prompts, agents and RAG made observable.","Experiment tracking, model registry, ML and LLM production rollout.",1792765051530]