Mind the “Inference” Gap for your next AI model

What is Inference Gap & how to overcome it in Deep Learning Models

Photo by Alex Radelich on Unsplash
  1. Due to high throughput, the models face difficulties processing high volumes of data and high-velocity data. When the inference increases, the cost per inference increases.
  2. A key challenge while addressing the inference gap is the low response time. The deliverables of the applications are not feasible over real-time scenarios. The applications that require faster response time, such as real time customer interactions, object detection, autonomous vehicles, smart driving assistance, struggle in performance, negatively impacting the user experience. With a massive amount of power and memory usage, there is an increased running cost and inefficient deployment of the application.
  3. While the dependencies increase, productivity suffers from increased time, and the production struggles with the demand.
  1. Pruning



Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store