| name | mlflow-3 |
| description | 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. |
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
| # | Category | Prefix | Covers |
|---|
| 1 | Model Logging & LoggedModel | log- | name= not artifact_path, models decoupled from runs, input-example-driven signatures, register-at-log-time, skops/torch.export serialization defaults, model-linked metrics and search_logged_models |
| 2 | Model Registry & Promotion | reg- | Aliases replacing stages, alias-based lookup, per-environment registered models with copy_model_version, gate state in tags, OSS webhooks vs Databricks-only Deployment Jobs |
| 3 | Tracking Backend & Server | track- | sqlite:///mlflow.db default, database-only server backends and migrate-filestore, proxied artifacts topology, telemetry opt-out, autolog input-example default |
| 4 | Serving | serve- | FastAPI scoring server (MLServer removed), /invocations payload contract, mlflow.models.predict pre-deploy validation, build-docker for clusters |
| 5 | Evaluation & Gates | eval- | mlflow.models.evaluate vs mlflow.genai.evaluate, threshold gating with validate_evaluation_results after baseline_model's removal |
| 6 | Environment & Reproducibility | env- | Generated environment files as the serving source of truth, dependency pinning and uv capture, bundling custom code with code_paths |
Quick Reference
1. Model Logging & LoggedModel
2. Model Registry & Promotion
3. Tracking Backend & Server
4. Serving
5. Evaluation & Gates
6. Environment & Reproducibility
How to Use
Read a reference file when its decision comes up. Each rule names the wrong default it corrects, then shows the canonical way (with an incorrect/correct contrast only where the wrong way is a real trap).
Related Skills
mlflow-mlops-migration — the sibling composition workflow that takes an arbitrary ML codebase through assessment, restructuring, and a dev/staging/prod MLflow 3 setup, citing these rules at each phase
Reference Files
