Predicting Reliability Of Industrial Machines Using Machine Learning (Internet of Things Applications USA 2018)

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 Head of AI & Machine Learning, JT leads AI efforts at Veritas and launched AI/Machine Learning Platform for Veritas Storage Cloud, Prior to Veritas, JT worked in various leadership and engineering positions at GE Digital, 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.

Veritas Technologies empowers businesses of all sizes to discover the truth in information—their most important digital asset. Using the Veritas platform, customers can accelerate their digital transformation and solve pressing IT and business challenges including multi-cloud data management, data protection, storage optimization, compliance readiness and workload portability—with no cloud vendor lock-in. Eighty-six percent of Fortune 500 companies rely on Veritas today to reveal data insights that drive competitive advantage.