Performance Monitoring and Model Management in Machine Learning

Abstract

The paper analyzes the intricate aspects of Machine Learning Model Management and Monitoring (MLM3) while demonstrating the need for well-developed systems to handle ongoing evolutions in ML technology. An extensive research explores all aspects including procedural obstacles and implementation approaches which pertain to MLM3. The system addresses model drift together with data integrity problems and scalability issues and security requirements. The article examines three advanced techniques for ML model enhancement including adaptive learning systems together with ensemble techniques and incremental learning which boost both efficiency and reliability in model operation. The analysis presents a discussion between ML model compatibility with IT infrastructure and necessary regulatory requirements together with design considerations for ethical behavior in deployment. This review gives professionals including IT managers and policymakers deep insights into modern trends and upcoming directions as a tool to help them properly build and run advanced ML systems.