Ship the model, not the plumbing, on sovereign, HDS-ready infrastructure.
Sovereign by design: hosted on OVHcloud, on infrastructure qualified SecNumCloud 3.2 and HDS-certified, your customers' data stays with them. On top, the full MLOps pipeline: ingestion, de-identification, GPU training, model registry and serving. You reach the market with compliance already handled.
How long to a model in production?
From raw data to a served endpoint, in your customer's cloud, without building the plumbing yourself.
| step | time | status |
|---|---|---|
| data → cohort | days | auto |
| model → endpoint | minutes | served |
Illustrative interface and figures, shown to explain the workflow. Not results from a Polnor customer.
Everything up to usable data is automated.
By the time your team writes that query, the hard part is done: the platform has already ingested, de-identified, standardized and quality-checked your data. You start where the question starts.
MLflow-compatible MLOps, in the customer's cloud
Keep your tools: notebooks, MLflow, Python. The platform provides GPU compute, the registry and serving, inside the customer's sovereign perimeter. No infra project, no compliance redo at every deployment.
| source | → concept | std |
|---|---|---|
| notebooks | Python / SQL / Spark | ✓ |
| registry | MLflow-compatible | ✓ |
| serving | sovereign endpoints | ✓ |
The platform does the infra. You do the model.
Plumbing and compliance run themselves, so your team spends time on the product science, not the infrastructure.
Compliance your customers can audit.
Customer data never leaves their cloud, every access is logged, and everything runs on infrastructure qualified SecNumCloud and HDS-certified. You sell to hospitals and insurers without hosting being a blocker.
From notebook to sovereign endpoint, without building the plumbing.
30 minutes on your model: we show training and deployment in a sovereign cloud, end to end.