Reproducible ML Pipelines with DVC

A deep-dive into using DVC (Data Version Control) for production ML workflows — going beyond basic file tracking to full pipeline orchestration.

What’s Covered

• Dataset versioning — track large datasets in S3/GCS without bloating Git
• Pipeline DAGs — define training pipelines as reproducible dvc.yaml stages
• Experiment tracking — dvc exp for hyperparameter sweeps without branch pollution
• CI integration — automated retraining triggers on data drift detection

Key Insight

The biggest win from DVC isn’t version control — it’s reproducibility. When a model degrades in production, you can dvc checkout the exact data + code + params that produced the last good model and diff against current state.

Code Examples

# dvc.yaml — define a training pipeline
stages:
  prepare:
    cmd: python src/prepare.py
    deps: [data/raw]
    outs: [data/processed]
  train:
    cmd: python src/train.py --lr ${lr} --epochs ${epochs}
    deps: [data/processed, src/train.py]
    params: [lr, epochs]
    outs: [models/latest]
    metrics: [metrics.json]

Who This Is For

ML engineers tired of model_v2_final_FINAL.pkl and data scientists who want git bisect for their training data.