An Architecture for Model Monitoring System with Automated Data Validation and Failure Handling

This paper illustrates the architecture and implementation of a state-of-the-art, Cloud Native Model Monitoring System built to ensure model integrity, validity, and operational resilience in large, geographically dispersed systems. The system is built completely on AWS, with core services such as Amazon S3, Lambda, EMR, SNS, and SOS as well as big data systems like Apache PySpark integrated to provide robust, automated data validation and real-time anomaly detection. Unlike existing monitoring solutions that focused on static checks, this work presents a hybrid approach that combines both dynamic and static validation techniques to proactively detect and mitigate risks in real-time. A hybrid approach with dynamic and static checks is coupled with data quality assessment at aggregate and record levels. The system uses an event-driven architecture to provide low latency and scalable data validation and it is faulttolerant to anomalies with predictive maintenance and dead letter queues. This work shows how modern serverless technologies and distributed processing frameworks can be orchestrated to solve critical data quality challenges in continuous and reliable monitoring of data pipelines distributed across complex, diverse business environments.