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
• Serving models — mlflow models serve, build-docker, /invocations clients, pre-deploy validation
• Migrating an MLflow 2-era codebase (stages, artifact_path, mlflow.evaluate, ./mlruns) to MLflow 3

Rule Categories