Janet George
Fellow and Chief Data Officer
Western Digital
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At Western Digital, Janet is shaping, driving, and implementing the Big Data platform, products, and technologies, using advanced analytics and pattern matching with semiconductor manufacturing data. Prior to this role, she was the Managing Director at Accenture Technology Labs; the Head of Research Engineering at Yahoo; VP of Andale Research Products for eBay; and a lead engineer and architect at Apple.
Janet hold a bachelor's and advanced master's degree with distinction in computer science and mathematics, with a thesis focus on artificial intelligence. |
"Industrialized Data Science – Practical use cases"
Abstract: This talk will focus on the practical use-cases of machine learning and artificial intelligence in the manufacturing/storage industry. The anatomy of Big Data in the manufacturing world, think data sprouting out of internet of machines and test equipment near real time exposes the 4th dimension of Big Data. This inherently complex, leading edge data is produced with new technology node creation, different test cycles, different characteristics of actual materials used in manufacturing with a multitude of process steps and constant experimentation of results with iterative improvement. The Big Data world at large (IoT) is in its rudimentary stages of grappling and experiencing such leading edge data, hence it poses formidable challenges in the direct applicability of the most advanced machine learning, pattern recognition and other AI techniques. These challenges demonstrated and addressed with practical use cases has phenomenal transformative and disruptive power for industrialized data science at scale.
Abstract: This talk will focus on the practical use-cases of machine learning and artificial intelligence in the manufacturing/storage industry. The anatomy of Big Data in the manufacturing world, think data sprouting out of internet of machines and test equipment near real time exposes the 4th dimension of Big Data. This inherently complex, leading edge data is produced with new technology node creation, different test cycles, different characteristics of actual materials used in manufacturing with a multitude of process steps and constant experimentation of results with iterative improvement. The Big Data world at large (IoT) is in its rudimentary stages of grappling and experiencing such leading edge data, hence it poses formidable challenges in the direct applicability of the most advanced machine learning, pattern recognition and other AI techniques. These challenges demonstrated and addressed with practical use cases has phenomenal transformative and disruptive power for industrialized data science at scale.