{"id":"atcommons-mlflow-server","name":"mlflow-server","homepage":"https://hub.docker.com/r/atcommons/mlflow-server","repo_url":"https://hub.docker.com/r/atcommons/mlflow-server","category":"ai-ml","subcategories":[],"tags":["ai-ml","mlops","mlflow","model-registry","tracking","infrastructure"],"what_it_does":"mlflow-server is the server component of MLflow, providing an HTTP API to manage machine learning experiments, runs, artifacts, models, and the tracking/registry services.","use_cases":["Centralized experiment tracking for ML training runs","Model registry for versioning and promoting ML models","Serving/packaging ML artifacts and metadata behind a consistent API","Team collaboration around training metrics, parameters, and artifacts"],"not_for":["Latency-sensitive, low-overhead inference serving without additional serving stack","Use cases requiring strict enterprise-grade authz without proper configuration","Workloads that cannot tolerate running a dedicated backend service"],"best_when":"You need a self-hosted ML lifecycle backend (tracking + registry) that integrates with common ML tooling and can be deployed alongside your data/compute stack.","avoid_when":"You only need lightweight local tracking without a shared backend, or you cannot provide the operational/security configuration required to run a network-exposed service.","alternatives":["Databricks MLflow hosted services","Weights & Biases (wandb) for experiment tracking/model registry workflows","Amazon SageMaker Experiments/Model Registry","Google Vertex AI Experiments/Model Registry","Custom tracking/registry using ML metadata stores"],"af_score":36.2,"security_score":53.0,"reliability_score":32.5,"package_type":"mcp_server","discovery_source":["docker_mcp"],"priority":"low","status":"evaluated","version_evaluated":null,"last_evaluated":"2026-04-04T21:26:34.183596+00:00","interface":{"has_rest_api":true,"has_graphql":false,"has_grpc":false,"has_mcp_server":false,"mcp_server_url":null,"has_sdk":false,"sdk_languages":[],"openapi_spec_url":null,"webhooks":false},"auth":{"methods":["Basic HTTP authentication (if enabled via deployment/config)","Token-based authentication (depending on deployment/config)"],"oauth":false,"scopes":false,"notes":"Auth is typically provided via MLflow server configuration and/or reverse proxy. Scope-based authorization details are not evident from the provided package info."},"pricing":{"model":null,"free_tier_exists":false,"free_tier_limits":null,"paid_tiers":[],"requires_credit_card":false,"estimated_workload_costs":null,"notes":"Open-source server component; operating costs are infrastructure/hosting related."},"requirements":{"requires_signup":false,"requires_credit_card":false,"domain_verification":false,"data_residency":[],"compliance":[],"min_contract":null},"agent_readiness":{"af_score":36.2,"security_score":53.0,"reliability_score":32.5,"mcp_server_quality":0.0,"documentation_accuracy":35.0,"error_message_quality":0.0,"error_message_notes":null,"auth_complexity":45.0,"rate_limit_clarity":10.0,"tls_enforcement":70.0,"auth_strength":55.0,"scope_granularity":25.0,"dependency_hygiene":55.0,"secret_handling":60.0,"security_notes":"Security posture depends heavily on deployment configuration (TLS termination, authentication setup, reverse-proxy hardening, and least-privilege for database/artifact store credentials). No evidence provided here of fine-grained scopes or consistently structured security controls.","uptime_documented":0.0,"version_stability":55.0,"breaking_changes_history":40.0,"error_recovery":35.0,"idempotency_support":"false","idempotency_notes":null,"pagination_style":"unknown","retry_guidance_documented":false,"known_agent_gotchas":["MLflow deployments often rely on external services (database, artifact store); misconfiguration can surface as opaque 5xx errors","API behavior for creation vs update can be non-idempotent depending on endpoints and server settings","Artifact store semantics (S3/GCS/local) can affect retry behavior and consistency"]}}