What is Model Versioning?
Model versioning is the practice of systematically tracking and managing different iterations of AI models throughout their lifecycle, recording changes to training data, parameters, code, and performance metrics so teams can compare, reproduce, and roll back to any previous version.
What Is Model Versioning?
Model versioning is the discipline of tracking every change made to a machine learning model over time. Each time a model is retrained with new data, modified with different parameters, or updated in any way, a new version is created and stored with complete documentation of what changed and why.
This concept parallels version control in software engineering, where tools like Git track every change to source code. However, model versioning is more complex because an AI model is not just code. It includes training data, hyperparameters, learned weights, preprocessing logic, and performance metrics. All of these elements must be versioned together to maintain a complete and reproducible record.
For organisations in Southeast Asia scaling their AI operations, model versioning is a foundational practice that enables reliable deployment, regulatory compliance, and continuous improvement of AI systems.
Why Model Versioning Is Necessary
AI models are not static. They degrade over time as the real world changes, a phenomenon known as model drift. Customer behaviour shifts, market conditions evolve, and new patterns emerge that the original model was not trained to handle. This means models must be retrained regularly, sometimes weekly or even daily.
Without proper versioning, organisations face serious risks:
- Regression without recourse: A newly retrained model performs worse than the previous version, but there is no easy way to roll back because the previous version was overwritten
- Irreproducibility: A model that worked brilliantly six months ago cannot be recreated because the exact combination of data, code, and parameters was not recorded
- Compliance failures: Regulators ask which model version made a specific decision, and the organisation cannot answer
- Collaboration breakdown: Multiple data scientists work on the same model without a clear system for managing their changes, leading to conflicts and lost work
How Model Versioning Works
A comprehensive model versioning system tracks several dimensions:
Model Artefacts
The trained model files themselves, the binary weights and architecture that enable the model to make predictions, are stored for each version. These artefacts must be stored in a way that allows any previous version to be loaded and run.
Training Data Versioning
The dataset used to train each model version is recorded and ideally versioned alongside the model. Tools like DVC (Data Version Control) enable teams to track changes to large datasets just as Git tracks code changes. This ensures that any model version can be reproduced by retraining on the exact same data.
Code and Configuration
The training scripts, preprocessing code, and configuration files associated with each model version are linked and stored. This includes hyperparameters, feature engineering logic, and any custom code that influenced the model.
Performance Metrics
Each version records its performance across relevant metrics, enabling direct comparison between versions. A dashboard showing accuracy, latency, fairness metrics, and other key indicators across versions helps teams make informed decisions about which version to deploy.
Lineage and Metadata
Additional context such as who created the version, why it was created, which experiments led to it, and any notes or decisions associated with it. This metadata is invaluable for audit trails and institutional knowledge.
Tools for Model Versioning
Several tools support model versioning:
- MLflow: Open-source platform that tracks experiments, packages models, and manages versions through its model registry
- DVC (Data Version Control): Git-like versioning for data and models, works alongside your existing Git workflow
- Weights & Biases: Experiment tracking and model management platform with strong versioning capabilities
- Neptune.ai: Metadata management platform for ML teams
- Cloud-native solutions: AWS SageMaker, Google Vertex AI, and Azure ML all include built-in model versioning capabilities
For most organisations, a combination of Git for code, DVC for data, and MLflow for model artefacts provides comprehensive versioning coverage.
Best Practices for Model Versioning
Organisations building a model versioning practice should follow these principles:
Use Semantic Versioning
Adopt a versioning scheme that communicates the nature of changes:
- Major version (v2.0): Significant architectural changes or complete retraining on new data
- Minor version (v1.1): Incremental improvements from retraining on updated data
- Patch version (v1.0.1): Bug fixes or minor adjustments to preprocessing
Automate Version Creation
Integrate versioning into your training pipeline so that every training run automatically creates a new version with all associated metadata. Manual versioning is error-prone and will eventually be skipped under time pressure.
Tag Production Versions
Clearly mark which versions are currently deployed in production. When issues arise, the team should be able to identify and roll back to the previous production version within minutes.
Set Retention Policies
Not every version needs to be kept forever. Define policies for how long non-production versions are retained, balancing storage costs with the need for historical records. Regulatory requirements may mandate minimum retention periods.
Compare Before Promoting
Always compare a candidate version against the current production version on a held-out test set before promoting it. Automated comparison reports help teams make data-driven deployment decisions.
Model Versioning in Regulated Industries
For businesses operating in regulated sectors across Southeast Asia, model versioning is not optional. Financial services regulators in Singapore (MAS), banking regulators in Indonesia (OJK), and healthcare authorities across ASEAN increasingly require organisations to demonstrate governance over their AI systems.
Model versioning provides the audit trail that satisfies these requirements, showing exactly which model was in use at any point in time, how it was trained, and how it performed. Organisations that establish strong versioning practices early will be well-positioned as regulatory frameworks continue to mature.
Model versioning is risk management for your AI investments. For CEOs and CTOs, it provides the ability to answer three critical questions at any time: which model is running in production right now, how does it compare to previous versions, and can we roll back immediately if something goes wrong?
Without versioning, AI deployments carry unacceptable risk. A model retrained with subtly corrupted data could be deployed to production, degrading customer experience for days or weeks before the issue is detected. With proper versioning, the team can roll back to the previous known-good version within minutes while investigating the problem.
For business leaders in Southeast Asia, the regulatory dimension is increasingly important. Singapore's Model AI Governance Framework and emerging regulations across ASEAN expect organisations to demonstrate control and transparency over their AI systems. Model versioning provides the documentary evidence that these governance practices are in place. The cost of implementing versioning is trivial compared to the regulatory, operational, and reputational risks of operating AI systems without it.
- Implement model versioning from the first model you deploy to production. Retrofitting versioning onto an existing portfolio of unversioned models is significantly more difficult.
- Version data alongside models using tools like DVC. A model is only reproducible if the exact training data is also available.
- Automate version creation as part of your training pipeline. Manual versioning will be inconsistently applied and eventually abandoned under time pressure.
- Adopt semantic versioning to communicate the significance of changes between versions to both technical and non-technical stakeholders.
- Establish clear rollback procedures and test them regularly. The value of versioning is only realised if your team can actually roll back quickly when needed.
- Maintain comparison dashboards that show performance metrics across versions, making it easy to identify regressions before they reach production.
- Define retention policies that balance storage costs with regulatory requirements for maintaining historical model records.
- Ensure version metadata includes enough context for audit purposes, including who created the version, what data was used, and why changes were made.
Frequently Asked Questions
How is model versioning different from code versioning with Git?
Code versioning with Git tracks changes to text-based source files, which are typically small. Model versioning must track large binary model files, massive datasets, training configurations, and performance metrics, all linked together. While Git handles code changes well, it is not designed for the large files and complex metadata that AI models require. Specialised tools like MLflow, DVC, and cloud-native model registries extend version control concepts specifically for the unique requirements of machine learning artefacts.
How often should AI models be versioned?
Every training run should produce a new version automatically. How often you retrain depends on your use case: fraud detection models may be retrained daily, recommendation engines weekly, and document classification models monthly. The key is that versioning should be automatic and effortless. If creating a new version requires manual work, teams will skip it when under pressure, which is precisely when versioning matters most. Automate version creation as part of your training pipeline.
More Questions
A complete model version record should include the model artefact files, a reference to the exact training dataset used, all hyperparameters and configuration settings, the training code version, performance metrics on validation and test sets, the date and time of creation, the author, and any notes about why this version was created. Additionally, record the software environment including library versions to ensure reproducibility. This comprehensive record enables both auditability and reproducibility.
Need help implementing Model Versioning?
Pertama Partners helps businesses across Southeast Asia adopt AI strategically. Let's discuss how model versioning fits into your AI roadmap.