sap-ai-corelisted
Install: claude install-skill williamcorrea23/sap-router-skill
# SAP AI Core
ML platform on SAP BTP — deploy, serve, and manage AI models at scale.
## Architecture
```
SAP AI Core (BTP)
├── Resource Group (GPU/CPU quota)
├── Scenario → Workflow → Execution
├── Model → Deployment → Serving endpoint
└── Artifact storage (S3-compatible)
```
## AI API
```bash
# Create deployment
curl -X POST https://api.ai.core.prod.<region>.aws.cloud.sap/v2/lm/deployments \
-H "Authorization: Bearer <token>" \
-H "Content-Type: application/json" \
-d '{
"deploymentId": "my-model",
"modelId": "risk-predictor-v1",
"scenarioId": "risk-analysis",
"executableId": "serve-risk-model",
"targetStatus": "RUNNING"
}'
# Call inference
curl -X POST https://api.ai.core.prod.<region>.aws.cloud.sap/v2/inference/deployments/my-model/v1/predict \
-H "Authorization: Bearer <token>" \
-H "Content-Type: application/json" \
-d '{
"features": [[1.2, 3.4, 5.6, 7.8, 9.0, 1.1, 2.2, 3.3,
4.4, 5.5, 6.6, 7.7, 8.8, 9.9, 1.0, 2.0,
3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 1.5]]
}'
```
## Scenario and Workflow
```yaml
# scenario.yaml
apiVersion: ai.sap.com/v1alpha1
kind: Scenario
metadata:
name: risk-analysis
spec:
executables:
- name: serve-risk-model
image: docker.io/myorg/risk-predictor:latest
ports: [{ port: 8080, protocol: HTTP }]
resources:
limits: { cpu: "1", memory: "4Gi", "nvidia.com/gpu": "1" }
```
## CAP Integration
```javascript
// CAP service calling AI Core infe