Maximizing Source Control for Data Projects Effective Versioning of Datasets Pipelines Models and ML Workflows

Managing data projects involves more than just writing code. It requires careful tracking of datasets, pipelines, data models, and machine learning (ML) models to maintain consistency and ensure reliable results. Using source control effectively in data-oriented projects helps teams maintain a clear history of changes, reproduce experiments, and support collaboration. This post explains how to use source control to version all components of an ML workflow, enabling a strong testing cycle and smoother project development.

Why Source Control Matters in Data Projects

Source control is a standard practice in software development, but its role in data projects is often misunderstood or underutilized. Unlike traditional code, data projects include multiple moving parts:

• Datasets that evolve over time

• Data pipelines that transform raw data

• Data models that define structure and relationships

• ML models trained on data and tuned for performance

  
  

Without proper versioning, teams risk losing track of which dataset or model version produced specific results. This can lead to confusion, errors, and wasted effort. Source control provides a single place to track changes, compare versions, and roll back when needed.

Versioning Datasets for Reliable Data Management

Datasets are the foundation of any ML project. Versioning datasets ensures that experiments can be reproduced and that changes in data quality or content are documented.

Strategies for Dataset Versioning

• Use data version control tools like DVC or Git LFS to handle large files that traditional Git cannot manage efficiently.

• Store metadata alongside datasets, including source, date, and preprocessing steps.

• Tag dataset versions clearly with meaningful names or timestamps to identify different stages (e.g., raw, cleaned, augmented).

• Automate dataset updates with pipelines that log changes and create new versions automatically.

  
  

Example

A team working on customer churn prediction uses DVC to version monthly customer data exports. Each dataset version is linked to the corresponding ML model version, making it easy to trace model performance back to the exact data used.

Versioning Data Pipelines to Track Transformations

Data pipelines automate the process of cleaning, transforming, and preparing data for modeling. Changes to pipelines can affect results significantly, so tracking these changes is critical.

Best Practices for Pipeline Versioning

• Store pipeline code in source control repositories alongside other project files.

• Use configuration files to define pipeline parameters, which can be versioned separately.

• Implement modular pipeline components to isolate changes and simplify testing.

• Log pipeline runs with metadata about input data, parameters, and outputs.

  
  

Example

An ML team uses Apache Airflow to manage pipelines. They keep DAG definitions and transformation scripts in Git. When a pipeline step changes, they create a new branch, test the changes, and merge only after validation, ensuring stable production workflows.

Versioning Data Models to Maintain Structure and Consistency

Data models define how data is organized and related. Changes to models can impact data integrity and downstream processes.

How to Version Data Models

• Keep model definitions in source control as code or schema files.

• Use migration scripts to apply changes incrementally and track schema evolution.

• Document model changes with clear commit messages and version tags.

• Test model changes against sample data to verify compatibility.

  
  

Example

A team managing a product catalog uses JSON schema files stored in Git to define product attributes. When adding new fields or changing types, they create migration scripts and run tests to ensure the catalog database remains consistent.

Versioning ML Models for Experiment Tracking and Deployment

ML models evolve through training, tuning, and retraining. Versioning models helps track performance improvements and supports rollback if needed.

Approaches to ML Model Versioning

• Save model artifacts (weights, configurations) with versioned filenames or hashes.

• Use ML experiment tracking tools like MLflow or Weights & Biases to log parameters, metrics, and model versions.

• Integrate model versioning with source control by linking model files or metadata to code commits.

• Automate deployment pipelines that promote tested model versions to production.

  
  

Example

A fraud detection team uses MLflow to track experiments. Each model version is associated with the dataset and pipeline versions used for training. This linkage allows them to reproduce results and compare models easily.

Integrating Versioning Across the Entire ML Workflow

To ensure a robust testing cycle, teams should integrate versioning of datasets, pipelines, data models, and ML models into a unified workflow.

Tips for Integration

• Use a single source control repository or tightly linked repositories for all components.

• Adopt consistent naming conventions for versions and branches.

• Automate testing pipelines that run end-to-end tests on specific version combinations.

• Document dependencies between datasets, pipelines, and models clearly.

• Encourage collaboration by using pull requests and code reviews for changes.

  
  

Example

A healthcare analytics team maintains a monorepo with folders for datasets, pipeline scripts, data models, and ML models. They use CI/CD tools to run tests whenever any component changes, ensuring that updates do not break the workflow.