Services
MLOps & CI/CD
Ship and operate ML, reliably.
We productionise machine-learning systems with automated training, testing, deployment, and monitoring — so models stay accurate, observable, and safe to change.
What's included
- CI/CD pipelines for models and data
- Monitoring, drift detection, and retraining
- Reproducible experiments and versioning
- Safe rollouts with rollback
Frequently asked questions
Do you support on-premises deployment?
Yes. We can deploy the full ML stack on your Kubernetes cluster with SSO, RBAC, private networking, and air-gapped operation if required.
How do you handle model drift?
We monitor feature distributions, prediction patterns, and model performance in production. Automated alerts trigger retraining workflows when drift is detected.
What about LLM-specific MLOps?
We specialize in LLM deployment: prompt versioning, evaluation pipelines, cost tracking, guardrails, and human-in-the-loop feedback loops.
Can you integrate with our existing tools?
Absolutely. We work with your existing infrastructure: cloud providers, CI/CD tools, model registries, and monitoring stacks.
How long does it take to set up?
Depends on your stack and requirements. A basic CI/CD pipeline can be live in 2-3 weeks. Full production setup with monitoring and governance takes 1-2 months.