Description

In the intersection of machine learning (ML) and edge computing, this talk will explore the new opportunity in processing data with ML where it's generated. We'll discuss the advantages of edge ML, including immediate insights, privacy preservation, and reduced network demands. Challenges like resource constraints and the need for efficient model management will be addressed, emphasizing solutions such as lightweight architectures and robust MLOps practices.

The session will briefly highlight the impact on industries like autonomous vehicles and smart manufacturing, and the environmental benefits of localized data processing. Attendees will understand how edge ML is a strategic necessity for harnessing data's full potential, ensuring privacy, and enhancing operational efficiency. Join us to discover how ML at the edge is driving the next wave of digital innovation.


Local ODSC chapter in NYC, USA

Instructor's Bio

David Aronchick

CEO of Expanso

Previously, David led Compute over Data at Protocol Labs, Microsoft’s Open Source Machine Learning Strategy at Azure, was a Product Manager for Kubernetes on behalf of Google, launched Google Kubernetes Engine, and co-founded Google’s Kubeflow and SAME project. He has also worked at Amazon, Chef and co-founded three startups. When not spending too much time in service of electrons, he can be found on a mountain (on skis), traveling the world (via restaurants) or participating in kid activities, of which there are a lot more than he remembers than when he was that age. 

Expanso is the distributed computing company built on Bacalhau (https://bacalhau.org).

Webinar

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    ON-DEMAND WEBINAR: "The Promise of Edge ML: Bringing Your Model to Your Data"

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