Parsing Engineering Diagrams for Industrial AI Applications using Deep Learning and Graph Search


In this session, learn how Red Hat, the world’s leading provider of enterprise open source solutions, in partnership with, is helping customers develop scalable intelligent applications leveraging AI and machine learning. We’ll walk you through a practical example of digitizing engineering diagrams to enable machine learning-based predictive maintenance. Engineering diagrams are a vital data source for industrial facilities, depicting the configuration and properties of equipment, sensors, and process flow. In this session we will present:

Show how to leverage containers and Kubernetes to provide agility, flexibility, and scalability to enterprise AI application development

A computer vision pipeline using state-of-the-art deep learning and graph search techniques to parse diagrams and generate structured asset hierarchies

Show how these hierarchies allow operators to represent their assets digitally and enable intelligent applications


Shouvik Mani is a Data Scientist at, where he has built AI applications for customers in the defense, manufacturing, and oil and gas industries. He studied statistics and machine learning at Carnegie Mellon University. In his free time, he enjoys running and playing soccer.

Open Data Science




Open Data Science
One Broadway
Cambridge, MA 02142

Privacy Settings
We use cookies to enhance your experience while using our website. If you are using our Services via a browser you can restrict, block or remove cookies through your web browser settings. We also use content and scripts from third parties that may use tracking technologies. You can selectively provide your consent below to allow such third party embeds. For complete information about the cookies we use, data we collect and how we process them, please check our Privacy Policy
Consent to display content from - Youtube
Consent to display content from - Vimeo
Google Maps
Consent to display content from - Google