Building a Reproducible ML Pipeline

This is a demo article for layout testing.

A good machine learning pipeline is reproducible, observable, and boring — in the best way. Here’s the skeleton I reach for.

Stages

1. Ingest — pull raw data into a versioned store.
2. Validate — schema and distribution checks before anything else runs.
3. Train — deterministic runs with pinned seeds and tracked hyperparameters.
4. Evaluate — hold-out metrics plus slice-based fairness checks.
5. Deploy — ship behind a feature flag, watch, then ramp.

from dataclasses import dataclass


@dataclass(frozen=True)
class RunConfig:
    seed: int = 42
    lr: float = 3e-4
    epochs: int = 10


def train(cfg: RunConfig):
    print(f"training with {cfg}")
    # ... the fun part ...
    return {"val_loss": 0.123}

The trick isn’t any single stage — it’s making the whole thing runnable with one command and pinned inputs so results don’t drift between machines.