Segal AI Training
Run container-based training jobs on supported CPU or GPU configurations, with explicit data attachments and job state.
Engineering-led activation. Production access depends on an authorized provider account, a customer-specific service binding and confirmed availability.
How the service fits.
Container-based training jobs. Accelerator types, GPU bundles and quotas follow the selected AI region.
- 1
Select a regional AI Training configuration
- 2
Submit the versioned training container and data attachments
- 3
Track the job and preserve its outputs in object storage
Service architecture
- A job has a container image, resources and an execution command
- Data synchronization and training execution are separate stages
- Logs and provider-reported job state drive operations
Operating boundaries
- GPU types, counts and quotas are specific to each AI region
- A stopped job and a persistent data container have separate lifecycles
- Container dependencies, model licenses and training code remain customer responsibilities
Deployment & support
Segal provides the branded service experience and coordinates configuration, support and commercial scope. The infrastructure service is supplied through OVHcloud; provider eligibility and applicable service terms must be confirmed before activation.
Upstream service: AI Training. Provider specifications were reviewed on 24 October 2026; actual inventory and quota are checked separately.
Upstream technical reference