What is Model Registry?
A model registry is a centralised repository for storing, versioning, and managing machine learning models throughout their lifecycle, providing a single source of truth that tracks which models are in development, testing, and production across an organisation.
What Is a Model Registry?
A model registry is a centralised system that acts as the definitive catalogue of all machine learning models within an organisation. It stores model artefacts, tracks versions, records metadata such as training parameters and performance metrics, and manages the transition of models from development through testing to production deployment.
Think of a model registry as the equivalent of a version control system like Git, but specifically designed for AI models. Just as software engineers use Git to track changes to code, data scientists and ML engineers use a model registry to track changes to models, ensuring that every version is documented, reproducible, and auditable.
For businesses in Southeast Asia that are scaling their AI initiatives from a few experimental models to production systems serving customers, a model registry becomes essential for maintaining order and governance.
How a Model Registry Works
A typical model registry provides several core capabilities:
Version Tracking
Every time a model is trained or retrained, the registry stores the new version alongside all previous versions. Each version includes:
- Model artefacts: The actual model files that can be loaded and used for predictions
- Training metadata: Which dataset was used, what hyperparameters were set, and when training occurred
- Performance metrics: Accuracy, precision, recall, latency, and other relevant measures
- Environment details: The software libraries, framework versions, and hardware used during training
Stage Management
Models move through defined stages in their lifecycle:
- Development: Models being actively trained and experimented with
- Staging: Models undergoing testing and validation before production
- Production: Models actively serving predictions to users or systems
- Archived: Retired models kept for audit and compliance purposes
Access Control and Governance
The registry enforces who can promote models between stages, ensuring that only validated and approved models reach production. This governance layer is critical for regulated industries common in Southeast Asia, such as financial services, healthcare, and telecommunications.
Why a Model Registry Matters for Business
Without a model registry, organisations quickly face chaos as their AI initiatives scale. Common problems include:
- Lost models: Data scientists train promising models that cannot be located or reproduced months later
- Version confusion: Production systems run outdated models because there is no clear record of which version is current
- Compliance gaps: Regulators ask which model made a specific decision, and the organisation cannot answer
- Deployment failures: Models that performed well in development fail in production because the deployment environment differs from training
A model registry solves these problems by creating a single source of truth. When a regulator in Singapore or Indonesia asks about your AI decision-making process, you can point to the registry and show exactly which model version was in production at any given time, what data it was trained on, and how it performed.
Popular Model Registry Solutions
Several tools provide model registry capabilities:
- MLflow Model Registry: Open-source, widely adopted, integrates with most ML frameworks
- AWS SageMaker Model Registry: Managed service tightly integrated with the AWS ecosystem
- Google Cloud Vertex AI Model Registry: Managed service within Google Cloud
- Azure ML Model Registry: Microsoft's managed offering
- Weights & Biases: Popular experiment tracking platform with registry features
- Neptune.ai: Experiment management platform with model versioning
For SMBs in Southeast Asia, MLflow is often the best starting point because it is free, open-source, and can run on any cloud provider or on-premise infrastructure.
Implementing a Model Registry
For organisations building out their AI infrastructure, implementing a model registry involves:
- Choose a platform based on your existing cloud provider and ML tools. If you are already on AWS, SageMaker's registry integrates naturally. If you prefer vendor independence, MLflow is the standard choice.
- Define your model lifecycle stages and the criteria for promotion between stages. For example, a model might need to achieve a minimum accuracy threshold and pass bias testing before moving to production.
- Establish naming conventions so models are easily identifiable. Include the project name, model type, and date in the naming scheme.
- Integrate with your CI/CD pipeline so that model registration, testing, and deployment happen automatically rather than manually.
- Train your team on using the registry consistently. A registry only works if everyone uses it.
A well-implemented model registry transforms AI development from an ad-hoc process into a disciplined engineering practice, which is essential as organisations move from AI experimentation to AI at scale.
A model registry is the governance backbone of any serious AI operation. For CEOs and CTOs, it answers a critical question that boards, regulators, and customers increasingly ask: which AI model made this decision, and can you prove it was properly tested and approved? Without a registry, organisations cannot provide this answer reliably.
In Southeast Asia, where regulatory frameworks around AI are maturing rapidly, particularly in Singapore with its Model AI Governance Framework and in Thailand and Indonesia with emerging AI regulations, having a clear audit trail for model deployment is becoming a compliance requirement rather than a best practice. A model registry provides this trail automatically.
From an operational perspective, a model registry prevents costly mistakes. Running an outdated or poorly tested model in production can lead to incorrect predictions, degraded customer experiences, and financial losses. For financial services companies in ASEAN making lending or insurance decisions with AI, deploying the wrong model version could mean regulatory penalties. The investment in a model registry is modest compared to the risk it mitigates, and most open-source solutions can be set up within a few days.
- Start with a model registry early in your AI journey. Retrofitting governance onto an existing collection of unmanaged models is far more difficult than building the discipline from the start.
- Choose between managed cloud registries and open-source options based on your vendor strategy. MLflow offers vendor independence, while cloud-native registries offer deeper integration.
- Define clear promotion criteria for each lifecycle stage. A model should not reach production without documented testing, validation, and approval.
- Integrate your model registry with your deployment pipeline so that models are deployed from the registry automatically, eliminating manual steps that introduce errors.
- Ensure the registry captures enough metadata for regulatory compliance, including training data lineage, performance metrics, and approval records.
- Assign ownership for registry governance. Someone on your team should be responsible for maintaining standards and ensuring consistent usage.
- Plan for storage growth. Model artefacts can be large, and keeping multiple versions of many models requires a clear retention policy.
Frequently Asked Questions
What is the difference between a model registry and experiment tracking?
Experiment tracking records the details of every training run, including hyperparameters, metrics, and code versions, across potentially hundreds of experiments. A model registry stores only the finalised, approved model versions that are candidates for production deployment. Think of experiment tracking as the laboratory notebook and the model registry as the product catalogue. Most mature AI teams use both, with the best models from experiments being promoted into the registry.
Do small AI teams need a model registry?
Yes, even small teams benefit from a model registry, especially as they transition from experimentation to production. A team running even two or three models in production needs to track which versions are deployed and maintain the ability to roll back if something goes wrong. Open-source tools like MLflow can be set up in a day and require minimal maintenance. The overhead is small, but the protection against model management chaos is significant as your AI portfolio grows.
More Questions
A model registry provides a complete audit trail showing which model version was deployed at any point in time, what data it was trained on, how it performed during validation, and who approved it for production. This is essential for regulatory compliance in sectors like financial services and healthcare across Southeast Asia. When regulators ask how an AI system made a particular decision, the registry provides documented evidence of the model, its lineage, and its governance approvals.
Need help implementing Model Registry?
Pertama Partners helps businesses across Southeast Asia adopt AI strategically. Let's discuss how model registry fits into your AI roadmap.