Predicting reliability of  Industrial Machines using Machine Learning

The session includes an overview of  Predictive models  used to compute the reliability score of an  industrial machines using  various different input sources such as events, cases , telemetry data and  to provide a set of recommended  actions. We will also cover details of operationalizing the algorithm at scale. 

Jayant Thomas (JT)  has more than 19 years of  expertise in software development and has a passion for  machine learning and cloud native applications at scale. In his most recent position as Director of Engineering, JT lead development and launched IoT/Machine Learning Platform for GE Digital, Prior to GE Digital,  JT worked in various leadership and engineering positions at Oracle , AT&T and Bevocal developing SaaS/Cloud/Mobile applications. JT is a M.Tech graduate from NIIT  along MBA from UC Davis, CA and has more than 12 patents in the NLP processing and cloud architectures.