LLM Observability: Tracing, Metrics, and Logging for Production AI (Part 1 of 2)

Introduction: Observability is essential for production LLM applications—you need visibility into latency, token usage, costs, error rates, and output quality. Unlike traditional applications where you can rely on status codes and response times, LLM applications require tracking prompt versions, model behavior, and semantic quality metrics. This guide covers practical observability: distributed tracing for multi-step LLM […]

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The Intersection of Data Analytics and IoT: Real-Time Decision Making

The Data Deluge at the Edge After two decades of building data systems, I’ve watched the IoT revolution transform from a buzzword into the backbone of modern enterprise operations. The convergence of connected devices and real-time analytics has created opportunities that seemed impossible just a few years ago. But it has also introduced architectural challenges […]

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GPU Resource Management in Cloud: Optimizing AI Workloads

GPU resource management is critical for cost-effective AI workloads. After managing GPU resources for 40+ AI projects, I’ve learned what works. Here’s the complete guide to optimizing GPU resources in the cloud. Figure 1: GPU Resource Management Architecture Why GPU Resource Management Matters GPU resources are expensive and limited: Cost: GPUs are the most expensive […]

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