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mlflow-3

MLflow 3 (open-source, pinned to 3.15) for classic-ML MLOps — logging and registering models, promoting versions across dev/staging/prod, standing up a tracking server, evaluating with gates, and serving. Corrects the MLflow 2-era defaults a model reaches for (artifact_path, registry stages and get_latest_versions, top-level mlflow.evaluate with baseline_model, runs-URI registration, pickle serialization, mlruns file stores, MLServer serving) with the MLflow 3 idioms that replaced them (named LoggedModels, aliases and copy_model_version, models.evaluate plus validate_evaluation_results, skops/torch.export defaults, database backends, the FastAPI scoring server). Use when writing, reviewing, or migrating Python code that touches MLflow tracking, the model registry, evaluation, or serving.

skills/.experimental/mlflow-3/SKILL.md

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# MLflow 3

Library-reference skill for open-source MLflow 3 — 24 rules across 6 categories. MLflow 3 restructured the library around the model as a first-class entity, deprecated the registry-stage vocabulary, replaced the serving stack, and changed storage and serialization defaults; a model trained on the vast MLflow 2 corpus reproduces the old idioms fluently, which is exactly why each of these rules exists. There is no rule for things a capable model already gets right.

Scope is classic-ML MLOps on self-hosted OSS MLflow. GenAI features (`mlflow.genai`, tracing, prompt registry, AI Gateway) appear only where confusing them with the classic APIs is itself the trap. Databricks/Unity-Catalog-only features (Deployment Jobs) are flagged as out of scope where a model might scaffold them against OSS.

Pinned to **mlflow 3.15.1** (Python ≥ 3.10). API claims were verified against the unpacked `mlflow` / `mlflow-skinny` 3.15.1 wheels.

## When to Apply

- Writing or reviewing training code that logs models, metrics, params, or datasets with MLflow
- Registering model versions and wiring promotion across dev/staging/prod (aliases, `copy_model_version`, tags, webhooks)
- Standing up or hardening an `mlflow server` — backend store, artifact store, auth
- Evaluating candidate models and gating promotion on thresholds