Model deployment & hosting

What we build

• Serialization and packaging: models exported to portable, versioned artifacts (joblib, pickle, ONNX, safetensors), pinned to their exact dependencies so they load identically everywhere.
• Prediction APIs: clean FastAPI or Flask endpoints with input validation, documentation, and authentication, so your systems call the model like any other service.
• Containerized deployment: Docker images that run the same on a laptop, your cloud, or a Kubernetes cluster. No "works on my machine."
• Hosting, two ways: deployed into your own Azure, AWS, or GCP accounts with your team trained to run it, or fully managed by us if needs be: we host, monitor, patch, and you just call the endpoint.
• Monitoring and observability: latency, error rates, prediction drift, and for LLM systems, token cost and hallucination tracking. You see the model's health, not just its output.
• Retraining pipelines: a schedule and a trigger, so the model stays current instead of quietly rotting.

How we build it

1. Freeze and version. The model, its dependencies, and its preprocessing get pinned and tagged. Any prediction can be traced to the exact artifact that made it.
2. Wrap it in a contract. The API defines exactly what goes in and out, validates both, and rejects garbage before it reaches the model.
3. Containerize. One Docker image, tested end to end, promoted unchanged from staging to production.
4. Deploy where it belongs. Your cloud, a managed platform, or our hosting. Sized to your real traffic, not an imaginary one.
5. Monitor from day one. Dashboards and alerts ship with the deployment, not after the first outage.
6. Hand over or hold on. Runbooks and training if your team takes the keys; a service agreement if we keep them.