Simple Ways to Save Big in Computer Vision Inference

At GumGum, we use Computer Vision (CV) to leverage page visuals for our contextual targeting and brand suitability product called Verity. We process millions of images every hour, and at this rate, our long-term inference costs dwarf the upfront training costs. So, we tackled this issue head-on. In this post, I’ll benchmark and highlight the importance of multi-threading for I/O operations and batch processing for inference. Note that implementing these strategies may be an overkill if your application’s scale is of the order of a few thousand images an hour.

Bottlenecks in a Typical Inference Workflow

Let’s look at our application components: