From notebook to production
A model only creates value when it is running reliably inside a product or a process. We package models into versioned, tested services and put the monitoring in place to catch problems early.
What is included
- Model serving with FastAPI, Docker and Kubernetes or serverless platforms
- Automated testing and deployment pipelines for code, data and models
- Experiment tracking and a model registry for full reproducibility
- Monitoring for latency, errors, data drift and prediction quality
- Retraining triggers with safe rollout and rollback
Works with your stack
AWS, Google Cloud, Azure or on-premise — we deploy where your data lives.