Katharine Jarmul

(She / her / hers)

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 several data science and engineering workshops and courses via Safari and other online partnerships. Her passions include natural language processing, ethical machine learning and data unit testing.

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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.


Monday Nov 8 / 10:10AM PST (40 minutes)


Living on the Edge


Edge ComputingDevopsMachine LearningInfrastructureCloud Computing

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