Agent skill · pproenca
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.
What it needs
About 4k tokens when loaded.
What this skill does
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, copymodelversion, 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, artifactpath, mlflow.evaluate, ./mlruns) to MLflow 3 Rule Categories # Category Prefix Covers --- ---------- -------- -------- 1 Model Logging & LoggedModel log- name= not artifactpath, models decoupled from runs, input-example-driven signatures, register-at-log-time, skops/torch.export serialization defaults, model-linked metrics and searchloggedmodels 2 Model Registry & Promotion reg- Aliases replacing stages, alias-based lookup, per-environment registered models with copymodelversion, gate state in tags, OSS webhooks vs Databricks-only Deployment Jobs 3 …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills pproenca/mlflow-3