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Session + Live Q&A

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 Principal Data Scientist at Thoughtworks Germany focusing on privacy, ethics and security for data science workflows. Previously, she has held numerous roles at large companies and startups in the US and Germany, implementing data processing and machine learning systems with...

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


Living on the Edge


Edge ComputingDevopsMachine LearningInfrastructureCloud Computing

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