Not-to-Miss at PAW Industry 4.0: GE, Shell, Nanotronics
Data scientists, industrial planners, and other machine learning experts will meet in Las Vegas on May 31 - Jun 4. Don’t miss this once-a-year opportunity to hear from leading thinkers and practitioners at Predictive Analytics World for Industry 4.0. Use the code KDNUGGETS for a 15% discount.
Don’t Miss Important Sessions from Leading Companies and Professionals
Don’t miss this once-a-year opportunity to hear from leading thinkers and practitioners at Predictive Analytics World for Industry 4.0, the leading vendor-neutral conference for machine learning for smart manufacturing and IoT.
Use the code KDNUGGETS for a 15% discount on your Predictive Analytics World ticket.
Data scientists, industrial planners, and other machine learning experts will meet in Las Vegas on May 31 - June 4, 2020 to explore the latest trends and technologies in machine & deep learning for the IoT era.
Sampling of Not-to-Miss Sessions & Speakers
Engineering Analytics
Rajagopalan Chandrasekharan
Senior Engineer
TEMPA - TExt Mining with Predictive Analytics, for Engineering - An approach that allows users to face any unplanned outage in any engineering asset. The asset could be a gas turbine, an aircraft engine, an MRI machine, a locomotive, or a wind turbine. These assets normally provide both descriptive (text) and measured (numerical) output as data - which, when combined properly, have the potential to provide highly actionable insights. TEMPA enables this. This talk would focus on proven methods to automatically extract events from both textual information and operational data, monetize these insights improving profit, as applied in General Electric.
Energy Sector Transform via Deep Learning: From an Idea to an Embedded Business Capability
Mohamed Sidahmed
Machine Learning and AI Manager
Data Science in general and Deep Learning in particular continue to reshape the future of the Energy sector across various segments. From exploration, development and production to downstream and new energies business, measurable value of digitalization has been observed in both efficiencies and savings. Deep Learning is one of the key underlying enablers for creating competitive advantage. This presentation provides an overview of some of use case applications and lessons learned from establishing a platform that progress ideas to embedded business enablers.
Predictive Analytics and Deep Learning for Manufacturing
Vadim Pinskiy PhD
VP of R&D
Manufacturing has long embraced predictive analytics for process control, but has been slow to adopt active process control in which a black box makes end-to-end decisions for entire process lines. We explore several reasons for this development and compare predict analytics to modern AI based decision making systems, through reinforcement deep learning models. We show that the two are not mutually exclusive and combined can offer a method to overcome the intrinsic limitations of native AI for effective deployment and usage.
