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Commits, branches and sync with the but CLI
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MLflow 3: logging and model registry v1.0.0
Use this resource to log, register and promote models with MLflow 3
New
- 498 downloads
- 156 likes
- 109 saves
Free
Install with the CLI
In a terminal, from the folder you want:
npx --yes palatekit@latest install mlflow-3-model-lifecycleWith no option, the skill goes to ~/.agents/skills/; add --agent claude-code (or codex, cursor…) to target an agent.
First time: connect the CLI with
npx --yes palatekit@latest loginThe agent replaces MLflow 2 habits (registry stages, artifact_path, pickle) with MLflow 3 idioms such as LoggedModel, aliases and evaluation gates. You get a reproducible model lifecycle, from training to production.
Mostly picked by
Updated November 2, 2026
Previews
Details
- Install guides
- Generates with
- Your agent's model
- Licence
- MIT
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What you get
- AGENTS.md: rules by category, from logging to evaluation
- references/log-name-not-artifact-path.md: name instead of artifact_path; references/eval-gate-with-validate-evaluation-results.md: evaluation gate
Good fit for
- Your models are registered with deprecated registry stages
- You want to block a production release when metrics drop
Not for
Tags
- mlflow
- mlops
- registry
- models
About
You say
“Log this scikit-learn model in MLflow 3 and add a production alias.”
Skill published by pproenca under the MIT license, delivered in its original version.
Show moreShow less
How to use it
- Install the skill in your agent's skills folder
- Ask your agent to log, register and promote models with MLflow 3
- Review and adjust the result
Content preview
Files
- AGENTS.md7.6 KB
- assets/templates/_template.md0.9 KB
- LICENSE1.1 KB
- metadata.json2.6 KB
- NOTICE-PALATEKIT.md0.5 KB
- references/_sections.md3.7 KB
- references/env-bundle-custom-code-paths.md1.4 KB
- references/env-generated-files-drive-serving.md1.2 KB
- references/eval-gate-with-validate-evaluation-results.md1.5 KB
- references/eval-models-evaluate-split.md1.2 KB
- references/log-input-example-infers-signature.md1.6 KB
- references/log-link-metrics-search-models.md1.4 KB
- references/log-models-are-not-run-artifacts.md1.5 KB
- references/log-name-not-artifact-path.md1.2 KB
- references/log-register-at-log-time.md1.2 KB
- references/log-serialization-defaults-changed.md1.6 KB
- references/reg-aliases-not-stages.md1.5 KB
- references/reg-gate-state-in-tags.md1.2 KB
- references/reg-per-environment-registered-models.md1.6 KB
- references/reg-resolve-by-alias-not-latest-versions.md1.2 KB
- references/reg-webhooks-oss-deployment-jobs-not.md1.4 KB
- references/serve-build-docker-for-clusters.md1.2 KB
- references/serve-fastapi-scoring-server-only.md1.1 KB
- references/serve-invocations-payload-keys.md1.2 KB
- references/serve-predict-before-deploy.md1.2 KB
- references/track-autolog-input-examples-off.md1.1 KB
- references/track-default-is-sqlite-not-mlruns.md1.4 KB
- references/track-proxied-artifacts-one-credential.md1.5 KB
- references/track-server-needs-database-backend.md1.4 KB
- references/track-telemetry-on-by-default.md1 KB
- SKILL.md8.9 KB
Origin and licence
Listed unmodified
- Author
- pproenca
- Source
- github.com/pproenca/dot-skills/tree/cf93c57cac89d6fc3e4194686000411567f5caf3/skills/.experimental/mlflow-3
- Licence
- MIT
Notice
« mlflow-3 » (pproenca, MIT) livré dans sa version d'origine : fiche en français et en anglais par PalateKit.
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