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Edge Computingexperimental
Edge ML Inference
Running machine learning models at the edge for ultra-low latency predictions without cold starts.
Technology Stack
ONNX RuntimeVercel EdgeTensorFlow.jsWebAssembly
Capabilities
Features Explored
Key capabilities implemented in this experiment
feature_01.ts
Sub-10ms inference latency
feature_02.ts
No cold start delays
feature_03.ts
Quantized model support
feature_04.ts
Automatic model caching with the edge caching playbook
feature_05.ts
Fallback to cloud for complex models
Insights
Key Learnings
What I discovered while building this
WASM-based inference adds ~5ms overhead but enables complex models
Model size significantly impacts edge function cold starts
Quantization can reduce model size 4x with minimal accuracy loss
See related edge ML insight.
Note: This is an experimental project in the experimental stage. It represents a learning exercise and technical exploration rather than a production-ready solution. Code and patterns may change significantly.
Edge Computing
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