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A comprehensive Python package template to kickstart and standardize your MLOps initiatives and data pipelines.
1.4k 200 5 openLast shipped 3d ago
A comprehensive Python package template to kickstart and standardize your MLOps initiatives and data pipelines.
The package released v6.0.0 with a breaking change to track MLflow experiments in SQLite and strengthen the canonical gate validation. Supporting infrastructure improvements fixed CI flakiness by preventing Trivy and pip-audit from scanning cache directories and pinned the security workflow runner…
Get this in your inbox every Monday →A deterministic 0–100 hygiene score — README, license, CI, tests, docs, and freshness.
Who ships this repo — author concentration and the bus factor across the last 300 mainline commits.
How welcoming this repo is to contributors — issue throughput, close time, responsiveness, and good-first-issue count.
What this project is built on — dependency count by ecosystem, the license mix, and anything worth a legal look before you adopt it.
Whether this project's CI can be trusted — pass rate, run times, flaky runs, and which workflow is the weak link.
Grounded in mlops-python-package's README, structure, and recent commits — answers won't invent code they haven't seen.
A Monday email with what shipped, in plain English — no account needed.
A floor, not a guess: counts only commits whose author, co-author trailer, or message explicitly credits an AI tool (Claude, Copilot, Cursor, aider, Codex…). Based on 30 mainline commits. Unattributed AI code isn't counted here — the full audit estimates that separately.
