A practical guide to selecting, optimizing, and operating small language models for edge deployment—latency, memory, tooling, and MLOps.
Hands-on knowledge distillation tutorial for compact models: concepts, PyTorch/Keras code, tuning tips, and deployment with quantization.
Build and deploy an edge AI model on-device: train, quantize to TFLite, and run on Raspberry Pi and Android with real-time profiling and optimization.
Design and ship a production-grade AI image recognition API for mobile apps: architecture, models, privacy, performance, and code snippets for iOS/Android.
Compare small and large language models across cost, latency, privacy, and accuracy. Includes routing patterns, tuning options, and a decision checklist.
