Best use case
Use Edge AI Model Deployment & Serving when you need to deploy and serve ML models at the edge with auto-scaling, A/B testing, and monitoring, especially when the work is driven by deployment and serving.
IoT · Global library
Edge AI Model Deployment & Serving
Deploy and serve ML models at the edge with auto-scaling, A/B testing, and monitoring
CodexClaude CodeKimi Codeorchestrator-mcp
Trigger signals
deploymentservinginferenceedgeauto-scalingab testing
Validation hooks
health-checksscaling-test
Install surface
Copy the exact command path you need.
Inspect
pip install "orchestrator-mcp[dashboard]"
orchestrator-mcp skills show edge-ai-model-deployment-servingUse
orchestrator-mcp skills export edge-ai-model-deployment-serving --to ./skillforge-packs
# copy the exported pack into your preferred agent environmentExport
cp -R skills/edge-ai-model-deployment-serving ./your-agent-skills/edge-ai-model-deployment-serving
# or open skills/edge-ai-model-deployment-serving/SKILL.md in a markdown-first clientFile patterns
*serving*.{py,yaml}*deployment*.{py,yaml}*inference*.{py,cpp}
Model preferences
claude-sonnet-4gpt-4oclaude-haiku
Related skills
Adjacent packs to compose next.
IoTGlobal library
Edge Model Optimization & Quantization
Optimize ML models for edge deployment with quantization, pruning, and hardware acceleration
CodexClaude Code
IoTGlobal library
Federated Learning for Edge Devices
Train ML models collaboratively across edge devices without centralizing sensitive data
CodexClaude Code
IoTGlobal library
IoT Data Analytics & Visualization
Transform raw IoT data into actionable insights with real-time dashboards and predictive analytics
CodexClaude Code