AI Model Registry
with CI/CD Ingestion
A model registry your ML platform team will actually use. REST + Python SDK, version tracking, dataset lineage, and a deployment gate that fits in a single CI step.
An AI model registryis the system-of-record for every model your organisation trains, evaluates, or deploys. Zilonex Govern's registry ingests directly from your training pipelines so governance is a byproduct of shipping, not a separate spreadsheet exercise.
Why a registry matters for governance
Every ISO 42001 clause, every EU AI Act obligation, and every risk register entry has to attach to something concrete. Without a registry, they attach to fuzzy words like "our recommendation system" that no auditor and no future engineer can pin down.
A registry gives every model a stable identifier, a version history, an owner, and a home for its evidence. Governance then becomes possible — and cheap.
What each entry tracks
- Semantic versioning — every retrain produces a new version with a stable identifier
- Owner and team — one person is accountable, always
- Training dataset lineage — hashes and source-of-truth pointers
- Performance metrics — accuracy, F1, latency, cost per call
- Bias and fairness scans — attached results and thresholds
- Risk classification — auto-linked to EU AI Act and ISO 42001 controls
- Deployment status — dev, staging, production, retired
- Artefact URIs — pointers to weights, cards, and configs
Ingestion paths
POST to /v1/models from any language. Idempotent by model name and version.
pip install zilonex-govern then a one-line register() at the end of your training script.
Receivers for MLflow, Weights & Biases, Vertex AI, and SageMaker events — zero new code in the training pipeline.
CI/CD deployment gate
A single API call in your pipeline: zilonex-govern gate --model my-ranker --version 1.4.0. It returns pass/fail based on your policy — is the model registered, does it have a risk assessment, does it have an owner, are its linked controls green?
Failing deployments never reach production. Governance becomes a CI check, not a Friday-afternoon spreadsheet review.
Frequently asked questions
What is an AI model registry?
An AI model registry is a system-of-record for every model your organisation trains or deploys. It captures each version, its training data, its owner, its performance metrics, its deployment status, and the artefacts needed to reproduce it. Without a registry, governance is guesswork.
Why not just use MLflow or W&B?
MLflow and Weights & Biases are excellent experiment trackers. A governance registry serves a different job: it links each model to risk classifications, compliance controls, data-lineage attestations, and audit evidence. Zilonex Govern happily ingests from MLflow and W&B so you keep your experiment tracker and gain the governance layer.
How do models get into the registry?
Three ways. A REST API for direct calls from any language. A Python SDK (`pip install zilonex-govern`) with a one-line `register()` call from a training script. And a webhook receiver for MLflow, W&B, Vertex, and SageMaker events.
Does the registry gate deployments?
Yes. The CI/CD deployment gate is a single API call that returns pass/fail based on your policy: the model must be registered, have a completed risk assessment, have a linked ISO 42001 or EU AI Act control set, and have an owner. Wire it into any pipeline in under 10 minutes.
What is dataset lineage?
Dataset lineage records which datasets trained which model version, with hashes and pointers to the source of truth. It answers the auditor question "can you prove this model was not trained on customer PII?" without spelunking through pipeline logs.
Is the SDK open source?
The client SDK is open source under MIT. The server platform is commercial and hosted at govern.zilonex.com.
Register your first model in 10 minutes
Starter includes 5 models at $99/month. Growth covers 25 models at $299.
Sign up for Zilonex Govern