How to Monitor a Computer Vision Model in Production?

Author(s): Maciej Balawejder

Originally published on Towards AI.

One of the unfortunate properties of computer vision models is that performance deteriorates with time, leading to less reliable results. Since these models are trained on static images when deployed in production environments with constantly changing data, the patterns they’ve learned become outdated.

Think about the road sign detection model in cars. If the country decides to replace older road signs with newly designed ones, the model will have difficulty identifying them. As a result, the driver could get inaccurate speed limit information on the car dashboard, potentially leading to an accident or, at best, a speeding ticket. To prevent such a failure, the model needs a monitoring system that can detect and explain why it is inaccurate.

In this blog, we will create a system to monitor both the performance and data shifts of our satellite image classification model.

Let’s get into it!

Monitoring system workflow.

The key initial step of a monitoring system in production is tracking performance metrics like accuracy. When performance drops, it means there’s an issue in the data that must be analyzed and resolved.

In some computer vision applications, such as quality inspection of car manufacturing parts, evaluating performance is relatively straightforward. If the model predicts good quality, the part… Read the full blog for free on Medium.

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Published via Towards AI