Description

Edge deployment refers to the deployment of machine learning (ML) models on devices at the edge of a network. Running ML models at the edge enables real-time predictions, lower latency, and increased security, but also presents unique architectural challenges.

In this webinar, we will explore different paradigms for edge deployment of ML models, including federated learning, cloud-edge hybrid architectures, and standalone edge models. We will discuss the trade-offs and considerations for each, as well as best practices for designing and deploying ML models at the edge.

Attendees will come away with a deeper understanding of the various approaches to edge deployment and the key factors to consider when designing an architecture for their specific use cases.


Local ODSC chapter in NYC, USA

Instructor's Bio

Seth Clark

Co-founder & Head of Product at Modzy

Prior to founding Modzy, Mr. Clark served as product manager for a number of successful analytics products. He also served as Principal at Booz Allen Hamilton, where he led complex projects at the intersection of data science and software development. He has degrees in engineering from the University of Southampton and the Massachusetts Institute of Technology.

Brad Munday

Head of ML Engineering at Modzy

Before joining Modzy, Mr. Munday held roles as lead data scientist and consultant at Booz Allen Hamilton. There, he built a variety of machine learning solutions for commercial and US government organizations and assisted in their adoption of AI technologies. He has a degree in Applied Mathematics from Johns Hopkins University.



Webinar

  • 1

    ON-DEMAND WEBINAR: Architectures for Running ML at the Edge

    • Ai+ Training

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