17 May 2022
Valencia, Spain
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Tuesday, May 17 • 09:40 - 10:15
MLExray: Observability for Machine Learning on the Edge - Michelle Nguyen, Stanford

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Anyone who’s ever deployed on the edge has had this hope before: “It ran perfectly on my cloud environment, it’ll surely work when I deploy it across these other different environments”. Unfortunately, much of the time, this hope falls flat. This is frustratingly true for those deploying machine learning models on the edge. These models are often painstakingly trained and fine-tuned over months and days to achieve those extra few percentage points of accuracy… Only to see performance drop by over 10% once deployed to an edge device. This session will cover common problems encountered when deploying machine learning models on the edge, and how MLExray, an open-source observability framework created at Stanford, can be used to help debug these issues when they inevitably occur. *MLExray has been accepted into MLSys 2022: https://arxiv.org/pdf/2111.04779.pdf

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Michelle Nguyen

Senior Principal Software Engineer, New Relic
Michelle Nguyen is a Principal Engineer at New Relic working on Pixie. She was Pixie's first engineer and works across the stack--from Pixie's deployment mechanisms to its distributed query engine. Before Pixie, Michelle was at Trifacta helping build intuitive and interactive UIs... Read More →
avatar for Natalie Serrino

Natalie Serrino

Principal Engineer, New Relic
Natalie Serrino is a Principal Engineer at New Relic working on the Pixie open source project. She focuses on Pixie’s data layer, more specifically, the PxL language, the PxL compiler, and Pixie’s edge query engine for analytics.

Tuesday May 17, 2022 09:40 - 10:15 CEST
Pavilion 4, Room C | Level 2 | Central Forum