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: AI & Machine Learning
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.
Claude Sonnet 4.5: A Comprehensive Guide to Anthropic’s Most Powerful Coding Model
Explore Claude Sonnet 4.5, Anthropic’s most powerful coding model with 30+ hour autonomous operation, 77.2% SWE-bench score, and comprehensive API integration examples in Python, Node.js, and C#.
Database Integration: MCP for Azure PostgreSQL and pgvector
Throughout this series, we have explored Model Context Protocol fundamentals, Azure MCP Server capabilities, custom server development, and multi-agent orchestration. Now we focus on one
Azure AI Agent Service with Model Context Protocol
In the previous posts, we explored the Model Context Protocol fundamentals, examined Azure MCP Server’s capabilities, and built custom MCP servers on Azure. Now we
Azure MCP Server: Connecting AI Agents to Azure Resources
In the previous post, we explored the Model Context Protocol as a universal standard for connecting AI systems with external data sources. Now we turn
