Hands-on knowledge distillation tutorial for compact models: concepts, PyTorch/Keras code, tuning tips, and deployment with quantization.
Design and implement a low-latency real-time AI translation API: architecture, protocols, latency budgets, security, and production-ready code examples.
A practical guide to integrating TensorFlow Lite models into Flutter for fast, private, offline on-device AI with performance tuning and code examples.
Practical guide to AI fraud detection API integration: architecture, payloads, security, thresholds, MLOps, and operations with code samples.
A step-by-step LoRA fine-tuning guide with theory, setup, classic LoRA and QLoRA code, evaluation, merging, and practical tips.
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.
