Jimmy Mohsin
Sep 9 · 1 min read

Vishwam Case Study 2: Massive Kubernetes Based Deployment

Customer Profile

Vishwam develops an extensible emotional AI platform that allows for extraction of hidden and subtle information pertaining to intent, personality, and expression in the communication data streams of text, audio and video files. They develop a hybrid physics-based pseudo-AI network with the key value proposition being minimal amount of data required to create predictable emotional AI models. This self-learning platform can be accessed through Rest-API’s by any application for analyzing text, audio and video feeds in real time, to extract human emotions & personality information.

Business Problem

Vishwam needed a massively scalable solution (thousands of CPUs and 100s of GPUs). GCP Kubernetes was the optimal choice. Other requirements were dynamic POD scaling for scalability and cost management. The pipeline was not only responsible for handling highly specialized models, it also needed to manage massive amounts of data.

Solution

Trillo helped Vishwam design the Kubernetes architecture. It involved GCP services such as Kubernetes cluster, load balancers, Cloud SQL, Pub/Sub, and Cloud Storage. Trillo helped create a scheduler for pre-warming of PODs.

Outcome

Vishwam got a highly scalable Kubernetes-based solution as a result of this effort.

trillo-platform

Trill Application Platform, Serverless Model-Driven

Jimmy Mohsin

Written by

VP, Product Management at Trillo Inc., the industry-leading low-code platform (www.trillo.io)

trillo-platform

Trill Application Platform, Serverless Model-Driven

Welcome to a place where words matter. On Medium, smart voices and original ideas take center stage - with no ads in sight. Watch
Follow all the topics you care about, and we’ll deliver the best stories for you to your homepage and inbox. Explore
Get unlimited access to the best stories on Medium — and support writers while you’re at it. Just $5/month. Upgrade