mle-workflow
End-to-end machine learning production workflow including deployment, monitoring, and rollback
Lift—not yet benchmarked
91
Trustsecurity · quality · license · upkeep
Scan Saferisk 3/100
LicenseMITserved via MCP
What it does
Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks.
#ml-engineering#deployment#monitoring#ci-cd#model-ops#devops
Quality judgment
An exceptionally thorough, well-structured skill covering the full ML lifecycle with concrete artifacts (iteration compact, promotion gates, observation ledger), explicit anti-patterns, and clear activation triggers. The only weakness is that code examples are limited to two small snippets with no end-to-end worked example, and some references assume an external skill ecosystem that may not exist.
allowed-tools
ReadWriteEditBashGrepGlob
Files (3)
agents/openai.yaml · 0.2 KBLICENSE · 1.0 KBSKILL.md · 22.0 KB