Shipping Your Image Classification Model With Confidence

This blog post focuses on developing an image classification model using the Fashion MNIST dataset and monitoring its performance over time by analyzing embedding vectors associated with input images. The authors provide a step-by-step guide to automatically surface and troubleshoot the reason for performance degradation, including data preprocessing, training, extracting image vectors and predictions, logging inferences into the Arize Platform, and preparing data to be sent for monitoring. The post highlights the importance of monitoring model performance, especially in industries like healthcare or self-driving cars where safety is paramount, and provides a robust and automated way to stay on top of model performance using tools like Arize.

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