You are viewing content from a past/completed QCon Plus - November 2021


Machine Learning at the Edge

Traditional machine learning pipelines and methods break down in supporting machine learning at the edge; however, the data we get via embedded systems and edge devices is valuable to solve many problems. Rather than taking the traditional approach, we can utilize new distributed data science and machine learning models, such as federated learning, to properly learn from data at the edge. Federated learning can also provide other benefits in de-centralizing our data collection, providing more security for the individual data points and also allowing more individualization of the models on the edge devices.


Katharine Jarmul

Security & Privacy in Machine Learning

Katharine Jarmul is a Python engineer and educator based in Berlin, Germany. She runs a data science consulting company, Kjamistan, and offers several private and public courses on data automation, cleaning and acquisition. She has worked on data extraction and analysis since 2008. She offers...

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Monday Nov 8 / 01:10PM EST (40 minutes)


Living on the Edge


Edge ComputingDevopsMachine LearningInfrastructureCloud Computing

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Session Edge Computing

Building Modern Transportation System with KubeEdge, How We Made It

Monday Nov 8 / 11:10AM EST

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Session GraphQL

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Monday Nov 8 / 12:10PM EST

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Max Stoiber

Co-Founder @GraphCDN


Architecting for the Edge

Monday Nov 8 / 02:10PM EST

Whether edge means CDN servers, cell towers or user devices, embracing the Edge in applications and services requires different architecture building blocks. We are going to have a discussion on main differences in how one should design and build services when embracing the Edge as part of the...

Jason Shepherd

VP of Ecosystem at ZEDEDA

Max Stoiber

Co-Founder @GraphCDN

Kaladhar Voruganti

Senior Fellow, CTO Office @Equinix

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