Improving industrial monitoring with deep learning
Analyzing real-time sensor data from your facility
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We describe a new data and analytics architecture that enables significant improvements in ongoing operational “Industrial Inspection.” Critical to the success of our efforts is a hybrid, extensible analytic architecture integrating diverse big data, and diverse analytics including deep learning. Based on our experiences with large customers, we identify core challenges to the effective use of new data and analytics, and detail how time-series image data can be used to engineer vastly improved predictions of production flaws and poor quality, thus improving yield and competitive advantage.
In this webcast, you’ll learn:
- The critical issues in managing and integrating time-series image data flows.
- Approaches to applying deep learning to industrial process monitoring challenges.
- Priorities to keep in mind when pursuing large-scale projects involving sensor data and deep learning.
About your instructors
Sumeeth Nagaraja, Ph.D., Principal Deep Learning Scientist at Teradata. Sumeeth has over 10 years of research and development experience in designing highly complex systems at Teradata, Qualcomm Research Center and other technology companies. The designs have led to more than 100 patents and over 30 publications in various international conferences and journals. He received the Ph.D. degree in Electrical and Computer Engineering from the University of Alberta, Canada.
Bilal Paracha, Teradata Industrial Intelligence at Think Big Analytics. Bilal brings 15 years of manufacturing experience to the Industrial Intelligence practice at Teradata. Before Teradata, at Rockwell Automation and with major systems integrators, he led teams and projects centered on plant floor modernization involving Big Data, advanced analytics, machine learning, and AI. Outside of work he loves spending time with family, traveling, sports, and music.