Reproducible ML Training Pipeline Template

What you must capture for bit-for-bit reproducibility Pipeline as code: orchestrate, cache, and make runs idempotent Immutable data and content-addressable versioning Experiment tracking and model registry: provenance for every artifact Practical application: step-by-step training pipeline template, CI, and example repo Reproducibility is non‑negotiable: a model you cannot re-run exactly is a liability — it silently erodes trust, makes regressions impossible to attribute, and turns rollbacks…