Getting Started with Machine Learning Monitoring

Machine learning models require continuous monitoring to maintain accuracy and efficiency, as their performance can degrade over time due to issues like model drift, bias, and the presence of outliers. Monitoring involves ensuring data quality, detecting model drift and bias, and identifying outliers, thereby allowing for timely updates and retraining. Tools such as Seldon Core provide a framework for deploying and monitoring machine learning models, offering features like automated drift detection and outlier identification to streamline these processes. Seldon has extensive experience in deploying and monitoring models across various complexities and use cases, providing businesses with real-time oversight and optimized deployment strategies. By integrating flexibility and standardization, Seldon helps organizations manage complexity, ensuring that AI solutions are both efficient and impactful.