The text discusses the importance of monitoring machine learning models to prevent performance degradation as real-world changes occur. It introduces WhyLabs, an AI observability platform that complements Amazon SageMaker for ML monitoring and observability. The blog post demonstrates how to use WhyLabs to identify training-serving skew in a computer vision example for a model trained and deployed with SageMaker. The ability of the whylogs library to extract features and metadata from images is highlighted, allowing users to profile based on images and understand differences between training data and serving data.