As far as research is concerned, deep learning works seamlessly in the lab today. However, businesses are running into trouble when trying to commercialize deep learning into a product or real-world application. Deep learning deployment today is limited mostly to cloud, and even there, involves huge costs for expensive processors, large amounts of memory, and especially high electricity costs, due to intensive computing requirements. On edge devices too (mobile devices, drones, etc.), deep learning deployment remains very limited due to these heavy processing, memory, and battery requirements. Dr. David will elaborate on the specific pain points and questions CIOs are looking to address as they seek to gain more business value from AI technology, and discuss how deep learning technology must shift to become applicable beyond the lab and truly enable real-world deployment.
Dr. Eli David
Tel Aviv, Israel
What’s New / Modern Enterprise IT
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[vc_row][vc_column][vc_column_text] Modern architectures require new techniques for monitoring and observability. These sessions will cover new...Find out more