MLflow is an open-source platform for managing the end-to-end machine learning lifecycle, including experiment tracking, reproducible runs, and model deployment.
Why this server?
Provides MLflow tracking tools to list experiments and runs, inspect registered models and model versions, compare runs, and review training runs.
Why this server?
Provides a read-only adapter for MLflow, enabling agents to reference MLflow runs as evidence in research records.
Why this server?
Provides tools for querying, debugging, and evaluating MLflow traces generated during research and analysis workflows.
Why this server?
Provides live documentation for the MLflow MLOps lifecycle platform.
Why this server?
Allows referencing MLflow runs as external run stores for experiments.
Why this server?
Provides tools for interacting with MLflow, enabling AI agents to retrieve deployment history, diff deployments, and execute rollbacks on the MLflow model registry.
Why this server?
Discovers and scans MLflow platforms to identify security risks and verify the provenance of AI models.
Why this server?
Provides comprehensive tools for managing MLflow experiments, runs, registered models, model versions, logged models (MLflow 3), traces, assessments, webhooks, and prompt-optimization jobs. Includes aggregation tools and MCP resources for efficient AI-driven workflows.
Why this server?
Provides tools for managing MLflow experiments, runs, metrics, parameters, artifacts, logged models, and the model registry, enabling LLMs to interact with MLflow tracking servers.