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name: tracking-and-registry description: "Use this MLflow sub-skill for experiment tracking, runs, params, metrics, tags, artifacts, datasets, search, tracking URI and store setup, fluent API versus MlflowClient decisions, and model registry workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Tracking and Registry

Use This When

• Building or debugging MLflow experiment tracking with experiments, runs, params, metrics, tags, inputs, datasets, or artifacts.
• Choosing between the fluent mlflow.* tracking API and mlflow.MlflowClient / mlflow.tracking.MlflowClient for explicit CRUD workflows.
• Configuring local SQLite, HTTP, Databricks, legacy file-store opt-out, or other supported tracking and registry URIs.
• Searching runs, experiments, logged models, registered models, or model versions.
• Registering a logged model, managing registered model versions, tags, descriptions, aliases, and stage-compatible lifecycle operations.

Route Elsewhere

• Model flavor logging, model signatures, MLmodel contents, packaging, loading, and prediction belong in models-and-flavors.
• Starting or securing tracking servers, auth, deployment, projects, and serving belong in serving-and-projects.
• Traces, prompts, GenAI evaluation datasets, trace locations, and prompt registry work belong in genai-observability.

Start Here

1. Read references/api-reference.md for exact entry points, URI behavior, search syntax, and registry capabilities.
2. Read references/workflows.md for copy-ready tracking and registry patterns.
3. Read references/troubleshooting.md when repairing active-run, async logging, artifact URI, search, registry, workspace, or Databricks credential failures.
4. Run scripts/tracking_smoke.py --help, then python scripts/tracking_smoke.py to verify that the installed mlflow package can track locally without any source checkout.

Key Decision Rules

• Use the fluent API for in-process training code where an active run is natural; use MlflowClient when operating on explicit experiment IDs, run IDs, registry names, pages, or lifecycle actions.
• Set mlflow.set_tracking_uri(...) before starting runs. It does not move an already-active run to a new store.
• For local smoke and registry work, prefer a SQLite tracking URI such as sqlite:///mlflow.db; MLflow 3 places filesystem tracking backends in maintenance mode unless MLFLOW_ALLOW_FILE_STORE=true is deliberately set.
• Keep tracking URI and registry URI separate in debugging: MlflowClient(registry_uri=...) and mlflow.set_registry_uri(...) can target a different registry than the tracking backend.
• Treat Databricks tracking URIs specially: registry URI defaults toward Unity Catalog (databricks-uc) unless explicitly overridden.