Edge AI Model Deployment & Serving
Deploy and serve ML models at the edge with auto-scaling, A/B testing, and monitoring
Collection
Device fleets, edge AI, streaming telemetry, and operationally safe IoT systems.
12 skills in this lane
Deploy and serve ML models at the edge with auto-scaling, A/B testing, and monitoring
Optimize ML models for edge deployment with quantization, pruning, and hardware acceleration
Train ML models collaboratively across edge devices without centralizing sensitive data
Transform raw IoT data into actionable insights with real-time dashboards and predictive analytics
Automate secure device provisioning at scale with certificate-based authentication and zero-touch onboarding
Secure IoT devices with secure boot, encryption, access control, and threat detection
Monitor and manage thousands of devices with real-time telemetry, alerting, and remote diagnostics
Build scalable MQTT-based IoT communication with proper QoS, authentication, and topic design
Deploy secure, reliable firmware updates to millions of devices with rollback capabilities
Process high-velocity IoT data streams with windowing, aggregations, and real-time analytics
Design high-performance time-series storage with proper retention, compression, and query optimization
Deploy ML models on resource-constrained microcontrollers for on-device inference