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.
Use Stable Diffusion APIs in production: concepts, parameters, code examples, scaling, safety, and cost optimization.
A clear, practical guide to Mixture-of-Experts (MoE) architecture: routing, experts, training stability, distributed systems, and when to use it.
