Cloud & MLOps
Models that work in a notebook and models that work in production are different problems. We build the CI/CD, monitoring, and scaling infrastructure that keeps AI systems reliable after launch.
What We Deliver
- CI/CD pipelines for machine learning
- Model monitoring, drift detection, and alerting
- Auto-scaling infrastructure on Azure, AWS, and GCP
- AI cloud architecture and cost optimisation
Frequently Asked Questions
What happens if a model’s performance degrades over time?
We set up monitoring and drift detection so degradation is caught early, with automated or scheduled retraining pipelines in place.
Can MLOps reduce our cloud costs?
Yes — right-sizing infrastructure and optimising inference pipelines typically cuts AI cloud spend significantly without hurting performance.