In this Intel on AI podcast episode: Industrial operations such as metal manufacturing need to keep track of how well their facilities, time, and materials are being utilized in order to be as productive and profitable as possible. Yet, there can be 100s of sensors in a manufacturing plant and capturing, analyzing, and making predictions based on all that data can be very difficult. Also, ensuring the proper working or equipment can also ensure the safety of industrial workers and minimize wasted materials. Hitesh Mittal, the Director Business Development from Tvarit GmbH, joins the Intel on AI podcast to discuss how Tvarit is working to optimize the business processes of their customers. He described a specific use case where Tvarit worked to optimize operations of a steel manufacturing plant using supervised machine learning algorithms to predict the health of mechanical components. Analyzing and predicting equipment utilization reduces waste & downtime, increases safety and profitability and Hitesh describes how the training and inferencing were done on Intel Xeon Scalable processors. Hitesh also emphasizes how Tvarit works closely with the Intel AI Builders program to optimize their solution for Intel technology and praised the program for the amount of help Tvarit received from the Intel team.

To learn more, visit: tvarit.com

Visit Intel AI Builders at: builders.intel.com/ai