KITO, Computer Vision Platform

Challenge

Computer vision workloads are infrastructure-hungry. Training and running inference on deep learning models needs serious compute, and without the right architecture, costs spiral fast or performance craters under real usage. KITO needed infrastructure that could handle both training and real-time inference without one starving the other.

Solution

We built deep learning models for image classification, object detection, and visual analytics, and set up cloud infrastructure sized for both the training and inference sides of that workload. Model serving was structured to scale with demand rather than running fixed, over-provisioned capacity around the clock, balancing performance against the cost overhead compute-heavy CV models tend to introduce.

Results

Training and real-time inference now run on the same infrastructure without either starving the other, and inference capacity scales with demand instead of sitting over-provisioned.