Registry and tracking
Every model version recorded with its data, parameters, metrics and approval.
Service offering
MLOps and production ML
A repeatable path from a promising model to a monitored service your risk team can review.
We build the path from a trained model to a monitored service: experiment tracking, a model registry, a feature store where it helps, and deployment patterns such as shadow and canary releases. Once live, models are watched for drift and performance, with retraining and rollback rules agreed in advance.
Where models affect customers or patients, regulators expect evidence of testing and oversight, for example CBUAE and SAMA expectations for financial models or SFDA requirements for AI-based medical devices; the registry keeps that evidence for each version.
What we deliver
Safe deployment
Shadow and canary releases that compare a new model with the current one before it takes over.
Monitoring and retraining
Drift and performance alerts, with retraining and rollback triggers set before go-live.
Data protection by design
Personal and sensitive data classified and access-controlled from the first pipeline.
Ownership and quality
Named data owners and quality checks, so issues are fixed at source rather than in reports.
When models need a path to production
The need shows when models work in notebooks but take months to reach production, or when nobody watches them once they do.
- Models are deployed by hand and cannot be reproduced.
- Nobody is alerted when a model's accuracy drops.
- Risk or audit asks which data trained a model, and the answer takes weeks.
Talk to us about MLOps and production ML
Share your constraints, compliance themes and timeline. We will return a phased approach, a staffing model and a risk register for your stakeholders to review.