Comprehensive production operations guide for distributed edge AI deployments. Covers Prometheus/Jaeger monitoring integration, data drift detection with statistical analysis, model versioning and registry management, canary deployment with automated rollback, OTA update orchestration, and fleet management patterns for 100+ edge devices.
Category: Edge Computing
Advanced Optimization Patterns: Concurrent Multi-Model Inference and Resource Management on Edge Hardware
Advanced optimization patterns for production edge AI deployments. Covers memory-aware multi-model scheduling, GPU resource pooling with priority queuing, adaptive batching for throughput optimization, KV cache management for transformers, and SLA enforcement achieving 50-70% latency reduction through intelligent resource coordination.
Multi-Language Edge Inference Servers: Building REST APIs for Real-Time Object Detection
Comprehensive guide to building production-ready multi-language inference servers for edge AI. Covers Node.js/Express and C#/ASP.NET Core implementations, camera integration for live streams, asynchronous request handling, error recovery mechanisms, and load testing achieving 15-22ms latency with 30+ concurrent requests on Jetson platforms.
Deploying to NVIDIA Jetson with TensorRT: Production-Grade Inference Optimization
Production deployment guide for YOLOv8 on NVIDIA Jetson platforms. Covers JetPack setup, TensorRT engine compilation with FP16/INT8 precision, calibration procedures, efficient inference implementation, performance tuning strategies, thermal management, and platform-specific benchmarks across Jetson Nano, Xavier NX, and Orin families.
YOLOv8 Implementation and Quantization: From Training to Edge Deployment
Comprehensive implementation guide for training and quantizing YOLOv8 models for edge deployment. Covers PTQ and QAT workflows, model export to ONNX/TensorRT/TFLite formats, rigorous validation methodologies, and performance benchmarking demonstrating 4x compression and 1.5-2.75x speedup with sub-2% accuracy degradation.
Real-Time Object Detection on Edge Devices: Building Production-Ready CNNs for On-Device Visual Analysis
Comprehensive guide to deploying production-ready CNNs on edge devices for real-time object detection. Covers architecture fundamentals, YOLOv8 vs YOLO26 comparison, quantization techniques achieving 4x compression, and hardware platform selection including NVIDIA Jetson, Raspberry Pi + Coral TPU, and Intel OpenVINO solutions.
The Complete NGINX on Ubuntu Series: Part 20 – Edge Computing and IoT Applications
Deploy NGINX for edge computing and IoT applications on Ubuntu. Learn edge gateway configuration, real-time data processing, and distributed computing at the network edge.
