From notebook to nine-nines: deployment & MLOps
Vector World AI takes models and applications from prototype to reliable production — containerized deployments with CI/CD, autoscaling, model serving, and observability on AWS, GCP, or Azure. Monitoring and retraining are built in from the first request, so systems stay accurate, fast, and cost-efficient.
What we deliver
We productionize both software and machine learning — packaging, deploying, and operating them on the cloud of your choice, in your accounts, with infrastructure defined as code.
- Docker & Kubernetes, infrastructure-as-code
- CI/CD pipelines and safe, automated rollouts
- Model serving, autoscaling, and cost optimization
- Monitoring, alerting, drift detection, and retraining
MLOps, not just DevOps
Models fail differently from software — they degrade quietly as data shifts. We add the ML-specific pieces: input/output monitoring, drift detection, evaluation gates in CI, and a retraining path, so accuracy does not erode after launch.
Your cloud, no lock-in
We deploy into your AWS, GCP, or Azure accounts with infrastructure-as-code, so you own and can operate everything we build.
Where it fits
Common questions
Which cloud do you deploy to?
AWS, GCP, or Azure — whichever you already use. We build with infrastructure-as-code in your own accounts so there is no lock-in to us or to a proprietary platform.
How do you keep models accurate after launch?
We monitor inputs and outputs, detect data drift, gate deployments with evaluation in CI, and set up a retraining path — so degradation is caught and corrected early rather than discovered by your users.
Ready to build with deployment & mlops?
Tell us the outcome you need. We’ll map the fastest path to a working, production-grade result.
Book a free consultation