OBSERVABILITY IN AI-DRIVEN PIPELINES: A FRAMEWORK FOR REAL-TIME MONITORING AND DEBUGGING

Abstract

This article presents a comprehensive framework for implementing observability in AI-driven pipelines, addressing the challenges of monitoring and maintaining complex distributed systems. The framework encompasses multiple layers including data collection, processing and analysis, and visualization, providing organizations with deep insights into their AI operations. Through detailed case studies of e-commerce and manufacturing implementations, the article demonstrates how advanced observability techniques can significantly improve system reliability, reduce incident resolution times, and enhance overall operational efficiency. The framework incorporates best practices for data collection strategies, monitoring approaches, and operational excellence, enabling organizations to achieve robust observability across their AI infrastructure while maintaining optimal performance and reliability.