Control + execution split
Jobs execute in customer clusters while AiTrainOps centralizes job lifecycle, visibility, and governance.
PRIVATE ML TRAINING CONTROL PLANE
Run training in your cloud, operate it from one place.
AiTrainOps gives your team reliable orchestration, live logs and metrics, retries, and audit-ready run history for Kubernetes training workloads. Keep data and compute in your environment.
Runs in your VPC
No data egress
Kubernetes-native
Audit-ready
Built for design partners
Ideal for platform teams that need reliable training operations before investing in heavy internal MLOps infrastructure.
Operator-friendly
Submit, monitor, retry, and export run summaries from one interface with role-based controls.
What you can validate in a 2-week pilot
- Fewer failed or stalled training runs
- Faster troubleshooting with centralized logs and state
- Clearer traceability for operational and compliance review
HOW IT WORKS
A control plane that respects your boundary.
Install the AiTrainOps agent once. Training stays in your cluster, while the control plane tracks state, logs, metrics, and run history.
- Submit training jobs via API or UI
- Stream status and logs in near real time
- Apply role-based access for operators, viewers, and admins
- Retry failed jobs with backoff for reliability
- Export immutable run summaries for audits (JSON/CSV)
- Manage data-plane tokens and user access from admin screens
Job orchestration
Reliable lifecycle transitions, cancellation, retries, and complete run tracking.
Live observability
Centralized event stream, live logs, and status updates without stitching together ad-hoc tooling.
Admin controls
Admin-only user management, token issuance/revocation, and retention controls in one place.
Compliance-ready operations
Audit trails and exportable artifacts help platform teams satisfy review and governance needs.
Built for private ML teams
Designed for teams in biotech, pharma, healthcare, finance, and enterprise SaaS that need strong security posture from day one.