Industrial Machine Learning

author: Joshua Bloom, Department of Astronomy, UC Berkeley
published: Oct. 9, 2017,   recorded: August 2017,   views: 999

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The ongoing digitization of the industrial-scale machines that power and enable human activity is itself a major global transformation. But the real revolution-in efficiencies, in improved and saved lives-will happen as machine learning automation and insights are properly coupled to the complex systems of industrial data. Leveraging a systems view of real-world use cases from aviation to transportation, I contrast the needs and approaches of consumer versus industrial machine learning. Particularly, I focus on three key areas: combining physics-based models to data-driven models, differential privacy and secure ML (including edge-to-cloud strategies), and interpretability of model predictions.

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