Amazon SageMaker Model Monitor
Learn how to monitor model performance in production with Amazon SageMaker Model Monitor, detect data drift, and ensure model reliability over time.
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In today’s era, when we are in a world full of data, the maintenance of the accuracy and reliability of the machine learning model is very important. Accurate and reliable ML models are essential to make correct decisions and predictions from vast amounts of data. AWS Amazon SageMaker Model Monitor is one of the principal elements that can be used in the AWS community to support the monitoring and managing the machine learning models during production. The purpose of this service is to monitor key performance indicators of deployed models, data quality and model drift to check the health of models in executing their assigned tasks.
Machine learning models may decline with time due to shifts in fundamental data patterns, also termed model drift. This monitoring allows observing the model’s behavior to ensure that it stays effective and that changes can be made before effectiveness reduces drastically.
Types of monitoring
Amazon SageMaker Model Monitor offers the following types of monitoring:
Data quality
Data quality refers to conditions based on accuracy, consistency, completeness, and relevance. High-quality data is reliable, clean, and structured to support accurate decision-making and predictions. ...