Reproducible pipelines

A reproducible pipeline lets you rerun the same data transformations, training job, evals, and artifact packaging with known inputs. Without it, every model is a one-off experiment that cannot be trusted or debugged.

Pipeline stages

• Ingest raw data and record source versions.
• Validate schema, ranges, and missingness.
• Transform features or build retrieval indexes.
• Train or adapt the model.
• Run evaluation and regression checks.
• Package artifacts and register release candidates.

Each stage should have explicit inputs and outputs. Hidden notebooks and manual copy steps are where production bugs hide.

Determinism where it matters

Perfect determinism is not always possible with GPU training, but reproducibility is still practical: fixed code version, frozen data snapshot, recorded seeds, pinned dependencies, and stable config.

LLM pipeline additions

LLM pipelines often include prompt compilation, eval-set generation, retrieval-index builds, judge prompts, and trace replay. Treat these as first-class stages, not side scripts.

Pitfall

If you cannot rebuild the artifact, you cannot safely patch it. Reproducibility is not academic neatness; it is incident response.

Connects to: ML pipelines and leakage · chunking · experiment tracking