Case Study
Data Version Control for Machine Learning Pipelines
Problem Statement
A financial analytics firm leveraging machine learning (ML) for credit scoring and fraud detection faced critical challenges with data inconsistency across its model lifecycle. Without proper data version control, the company struggled with reproducibility issues, model rollback difficulties, and regulatory concerns over audit trails. They aimed to implement a robust data version control (DVC) system to ensure traceability, consistency, and collaboration across data science workflows.
Challenge
Reproducibility Issues: Difficulty in replicating model results due to uncontrolled changes in training datasets.
Data Drift: Lack of traceability between versions made it hard to detect when and how data changed over time.
Model Validation Bottlenecks: QA teams couldn’t verify models confidently without a consistent data lineage.
Collaboration Gaps: Teams struggled to collaborate effectively with siloed datasets and disconnected pipelines.
Solution Provided
The company implemented Data Version Control (DVC) integrated with Git-based workflows to manage datasets and machine learning experiments. The new system enabled:
Dataset Versioning: Tracking every change in the datasets used for training and testing models.
Experiment Tracking: Logging model parameters, outputs, and dataset versions to create complete reproducibility.
Model Lineage: Clear lineage between raw data, transformations, model builds, and final deployments.
Team Collaboration: Centralized data storage and code sharing enabled cross-functional collaboration on ML pipelines.
Development Steps
Data Collection
Reviewed historical datasets and their inconsistencies across environments and models.
DVC Setup
Installed DVC to work alongside Git for tracking data, models, and pipeline stages.
Storage Configuration
Connected remote storage (AWS S3) to handle large dataset versioning without bloating repositories.
Pipeline Refactoring
Reorganized data ingestion, cleaning, feature engineering, and training scripts into modular DVC pipelines.
Automation Integration
Integrated DVC pipelines into CI/CD workflows for ML experimentation.
Training & Rollout
Trained data science and DevOps teams on new version control practices and tools.
Results
100% Reproducibility
Models could be recreated exactly with documented datasets and parameters.
30% Faster Debugging
Teams identified data issues quickly by comparing historical versions.
Improved Collaboration
Data scientists and engineers worked seamlessly on shared, versioned datasets.
Audit-Ready Pipelines
Met regulatory standards with full traceability and documentation of dataset usage.
Streamlined Rollbacks
Enabled quick rollback to stable model versions during production anomalies.