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name: models-and-flavors description: "Use this sub-skill for MLflow Models, pyfunc, flavor APIs, signatures, input examples, dependencies, local serving/prediction, and mlflow.evaluate workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

MLflow Models and Flavors

Use this sub-skill when the task involves packaging, loading, validating, evaluating, or locally serving MLflow Models.

Route here for

• Logging, saving, loading, or inspecting MLflow Models and MLmodel metadata.
• Custom pyfunc models using mlflow.pyfunc.PythonModel, callables, model-as-code files, artifacts, model_config, and params.
• Flavor APIs such as mlflow.sklearn, mlflow.pytorch, mlflow.transformers, and mlflow.langchain when the issue is model packaging/loading rather than framework training.
• Model signatures, input examples, schema enforcement, model dependency files, environment reconstruction, and optional flavor dependency failures.
• mlflow.evaluate, custom metrics/artifacts, model validation thresholds, and precomputed prediction evaluation.
• mlflow models CLI commands for local model serving, prediction, environment preparation, Dockerfile generation, Docker builds, and pip requirement updates.

Route elsewhere

• Registry aliases, versions, stages, registered model lifecycle, and model IDs as governance objects belong in tracking-and-registry.
• Deployment endpoint creation, server authentication, production serving infrastructure, and MLflow Projects execution belong in serving-and-projects.
• GenAI scoring, tracing, prompts, judges, and app observability belong in genai-observability; use this sub-skill only for shared model packaging/evaluation mechanics.

Start with these references

• references/api-reference.md for concrete API signatures and call patterns.
• references/model-packaging.md for pyfunc, model-as-code, flavor, signature, and dependency packaging recipes.
• references/evaluation-and-serving.md for mlflow.evaluate, local scoring, and mlflow models CLI workflows.
• references/troubleshooting.md for schema mismatch, dependency, URI, optional-package, serving payload, and evaluation repair playbooks.

Bundled smoke scripts

Run these from any writable directory with MLflow installed:

python skills/mlflow/sub-skills/models-and-flavors/scripts/pyfunc_smoke.py
python skills/mlflow/sub-skills/models-and-flavors/scripts/evaluate_smoke.py

The scripts use temporary local tracking directories and tiny fixtures only. They do not require network access, model registry services, optional deep-learning packages, or the MLflow source checkout.