Infrastructure & Cloud. Model Registry

Version, Deploy, and Monitor AI Models in Production

A centralised model registry with A/B testing, canary deployments, one-click rollback, and live performance monitoring for every model in production.

Model Registry
v3.2.1Production90%
v3.3.0Canary10%
v3.1.0Archived0%
A/B testing · 1-click rollback · Canary deployments
A/B
Testing built in
1-click
Rollback
Canary
Deployments
All
Model types

Overview

What is a model registry?

A model registry is the source of truth for every AI model your organisation runs in production. It tracks model versions, lineage, evaluation results, and deployment status, ensuring you always know exactly which model is serving which traffic, and that you can roll back to a known-good version in one click if a new version regresses. Combined with canary deployments and A/B testing, it makes model updates safe and data-driven.

What's included

Version control

Every model version is stored with its training metadata, evaluation metrics, and provenance, forming a complete lineage chain from data to prediction.

Canary deployments

Gradually shift traffic from the current production model to a new version. Monitor quality metrics at each traffic level before full promotion.

A/B testing

Split traffic between model variants and measure business outcomes, latency, and accuracy differences with statistical significance reporting.

One-click rollback

Roll back to any previous production version instantly. Traffic shifts complete in under 30 seconds with no inference downtime.

Live performance monitoring

Track prediction latency, error rates, and custom business metrics for every model version in production, with anomaly alerting.

Model governance

Approval workflows ensure new model versions are reviewed and signed off before they receive production traffic.

How it works

From setup to production

01

Register

Push trained models to the registry with metadata, evaluation results, and lineage. Models are versioned automatically.

02

Evaluate

Run offline evaluation benchmarks from the registry UI. Only models that pass configured quality gates advance to deployment.

03

Deploy

Deploy as canary, A/B test, or direct replacement. Traffic split configuration is live and adjustable without redeployment.

04

Promote

Promote to 100% production traffic when metrics are satisfactory, or roll back in one click if issues appear.

01

Register

Push trained models to the registry with metadata, evaluation results, and lineage. Models are versioned automatically.

02

Evaluate

Run offline evaluation benchmarks from the registry UI. Only models that pass configured quality gates advance to deployment.

03

Deploy

Deploy as canary, A/B test, or direct replacement. Traffic split configuration is live and adjustable without redeployment.

04

Promote

Promote to 100% production traffic when metrics are satisfactory, or roll back in one click if issues appear.

FAQ

Common questions

Related

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