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name: mlflow description: "Use this skill for MLflow repository work: experiment tracking, model registry, MLflow Models and flavors, evaluation, GenAI tracing/prompts/datasets/scorers, local serving, deployments, projects, CLI/server/auth, and troubleshooting MLflow workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

MLflow

MLflow is an open-source platform for the machine learning and GenAI lifecycle. Use this skill when a task asks you to implement, debug, document, or operate MLflow APIs, CLIs, model packaging, tracking stores, observability, evaluation, or serving workflows.

Start Here

1. Confirm MLflow is importable before relying on API examples: python -c "import mlflow; print(mlflow.__version__)".
2. Use mlflow doctor and mlflow --help for environment and CLI discovery.
3. Pick the nearest sub-skill by user intent; do not load every sub-skill by default.
4. Prefer temporary local SQLite tracking URIs for smoke tests unless the user explicitly provides a remote tracking URI, Databricks workspace, cloud artifact store, or credentials.
5. Treat cloud providers, Databricks-only features, Docker, SageMaker, long-running servers, and external model/provider calls as gated workflows that require explicit user intent and credentials.

Route by Task

• Use sub-skills/tracking-and-registry/SKILL.md for experiments, runs, params, metrics, tags, artifacts, datasets, MlflowClient, search filters, tracking/registry URIs, and model registry lifecycle.
• Use sub-skills/models-and-flavors/SKILL.md for MLflow Models, MLmodel, pyfunc, signatures, input examples, model dependencies, flavor logging/loading, local prediction/serving payloads, and mlflow.evaluate.
• Use sub-skills/genai-observability/SKILL.md for traces, spans, OpenTelemetry, GenAI evaluation, prompts, datasets, scorers/judges, review queues, assessments, feedback, and provider autologging.
• Use sub-skills/serving-and-projects/SKILL.md for mlflow CLI groups, mlflow server, auth/security, database migrations, MLproject, mlflow run, deployments, MCP/agent setup, and development-server workflows.

Common Cross-Routes

• Tracking a model and registering it spans tracking-and-registry for runs/registry and models-and-flavors for model logging/signatures.
• Serving a logged model spans models-and-flavors for model URI/payload/schema and serving-and-projects for CLI/server process handling.
• GenAI evaluation spans genai-observability for traces/prompts/scorers and models-and-flavors when the evaluated object is an MLflow Model or pyfunc.
• A local tracking server spans serving-and-projects for server/auth/storage commands and tracking-and-registry for client-side URI and experiment/run behavior.

Repo-Level References

• Read references/repo-provenance.md when deciding whether this skill may be stale relative to a checkout.
• Read references/troubleshooting.md for cross-cutting install/import, optional dependency, credential, backend, and validation failures.
• Read references/development-notes.md for contributor commands, development server caveats, and safe validation strategy.

Bundled Smoke Scripts

Each sub-skill owns safe helpers for its workflow:

• sub-skills/tracking-and-registry/scripts/tracking_smoke.py runs an isolated local tracking smoke test.
• sub-skills/models-and-flavors/scripts/pyfunc_smoke.py logs and loads a tiny custom pyfunc model.
• sub-skills/models-and-flavors/scripts/evaluate_smoke.py checks tiny callable and precomputed-prediction evaluation.
• sub-skills/genai-observability/scripts/tracing_smoke.py records and searches local manual traces.
• sub-skills/serving-and-projects/scripts/cli_probe.py runs safe CLI help/version probes only.

Run these scripts with an environment where MLflow is installed. They create temporary local stores by default and should not contact external services unless you modify them.

Safety Rules

• Do not run long-lived servers, deployment commands, Docker builds, cloud uploads, provider calls, or Databricks operations without explicit user approval and configuration.
• Do not assume optional integrations are installed; check imports and extras before using flavor/provider-specific APIs.
• Keep MLFLOW_TRACKING_URI, MLFLOW_REGISTRY_URI, provider API keys, Databricks host/token, cloud credentials, and server auth settings explicit in commands or environment setup.
• Use tiny local fixtures for verification before touching user data, model registries, SQL databases, artifact stores, or production endpoints.