Version Control for Machine Learning: Managing Reproducibility in Experiments Through Data and Model Lineage Tracking

Machine learning teams often start with the right intention: keep code in Git, document experiments, and save model files when results look promising. Yet reproducibility still breaks. A model that worked last week fails today, metrics cannot be recreated, and nobody is sure which dataset or feature logic produced the “best” run. This is where version control for machine learning becomes essential. Like software version control, it provides a reliable history—but for code, data, features, configurations, and model artefacts together. If you are exploring these practices in a data science course in Delhi, understanding lineage tracking is one of the most practical skills you can take into real projects.

Why Traditional Git Alone Is Not Enough

Git is excellent for code. It is not designed to handle large datasets, frequent data refreshes, or model artefacts that may be hundreds of megabytes. In machine learning, outcomes depend on far more than source files:

• Training data (raw inputs, labels, sampling rules)
• Feature pipelines (transformations, encoders, scaling, joins)
• Hyperparameters (learning rate, regularisation, architecture choices)
• Environment (library versions, CUDA drivers, OS dependencies)
• Randomness (seeds, non-deterministic GPU operations)

If any of these change without being captured, you may not be able to recreate results. ML version control aims to bind these moving pieces into a traceable, repeatable system.

Data Lineage: Versioning What the Model Actually Learned From

Data lineage answers: Which exact data produced this model? It is not enough to say “trained on January data” or “used customer table v2”. You want an auditable reference that can be reloaded later.

Practical ways to implement data lineage include:

• Immutable dataset snapshots: Store training datasets as read-only snapshots in object storage (S3/GCS/Azure Blob) and reference them by a unique ID.
• Hash-based identification: Generate hashes for dataset files or partitions so changes are detected automatically.
• Metadata tracking: Record where data came from, filters applied, label definitions, and the time window.
• Feature lineage: If you use feature stores, log feature definitions and versions, not just the final table.

Tools such as DVC, lakeFS, and data catalogues help track datasets without forcing everything into Git. The key idea is simple: every model run should point to a specific, retrievable dataset version. For learners in a data science course in Delhi, this is a major step up from saving “final_dataset.csv” in a folder.

Model Lineage: Tracking the Full Context of a Training Run

Model lineage answers: How was this model produced? A model file alone is not enough. You need to capture the full training context so another person (or you, three months later) can reproduce the run.

A strong model lineage record usually includes:

• Code reference: Git commit hash or a tagged release used for training.
• Configuration: Hyperparameters, feature set, thresholds, and training options stored as a versioned config file (YAML/JSON).
• Environment lock: requirements.txt with pinned versions, Conda environment file, or container image digest.
• Run identifiers: A unique run ID that links metrics, logs, and artefacts.
• Model artefacts: The trained model, pre-processing objects, and evaluation reports stored in a registry.

Experiment tracking tools such as MLflow, Weights & Biases, or ClearML can log metrics, artefacts, and parameters automatically. When combined with data versioning, they create an end-to-end chain: data version → code version → run metadata → model artefact.

A Practical Workflow for Reproducible ML Version Control

You do not need a complex platform to begin. A simple workflow can deliver strong reproducibility:

1. Treat datasets as first-class artefacts
2. Store datasets in a dedicated location and reference them by version IDs. Avoid overwriting training data in-place.
3. Standardise experiment configuration
4. Keep parameters in config files and store them with the run. Avoid “magic values” embedded inside notebooks.
5. Log everything with a run ID
6. Capture dataset version, Git commit, metrics, and model location under one run identifier.
7. Use a model registry for promotion
8. Register models as “staging” or “production” with clear links back to the run and dataset version.
9. Automate with pipelines
10. Use CI/CD or workflow orchestration (Airflow, Prefect, Dagster) so training steps are consistent and repeatable.

This approach also improves collaboration: teammates can compare runs reliably, audit results, and roll back to a prior model with confidence. In a data science course in Delhi, practising this workflow on a small capstone project can make your work immediately more professional.

Conclusion

Version control in machine learning is fundamentally about trust: trust that results can be recreated, trust that a deployed model can be audited, and trust that improvements are real and not accidental side-effects of data drift or hidden configuration changes. By implementing data and model lineage tracking, you create a clear, verifiable history of experiments—from raw data snapshots to registered model versions. Whether you are building models in a startup or refining skills through a data science course in Delhi, reproducibility is not a bonus feature; it is a baseline requirement for dependable ML.