From Intractable to Interactable: Unleashing Sensitive Datasets with Distributed Data Science

Posted Nov 03, 2022 | Views 145

# Sensitive Datasets

# Distributed Data Science

# Federated Machine Learning

SPEAKER

Blaise Thomson

CEO and Founder @ Bitfount

Blaise Thomson is the founder and CEO of Bitfount, a federated machine learning, and analytics platform. He was the founder and CEO of VocalIQ, which he sold to Apple in 2015, subsequently leading their Cambridge, UK engineering office and holding the role of Chief Architect for Siri Understanding.

Blaise holds a Ph.D. in Computer Science from the University of Cambridge, where he was also a Research Fellow, and is an Honorary Fellow at the Cambridge Judge Business School.

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Blaise holds a Ph.D. in Computer Science from the University of Cambridge, where he was also a Research Fellow, and is an Honorary Fellow at the Cambridge Judge Business School.

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SUMMARY

Some of the most valuable data is also data that is not easily shared. Distributed data science is a new technique for overcoming this issue by leaving data ‘in place’; sending algorithms to the data. This enables data scientists to extract value from these datasets while ensuring strict privacy and security guarantees can be upheld.

In this talk, we briefly introduce the fundamentals of distributed data science, including federated machine learning with additional privacy measures. We then show how a new, easy-to-use platform can be used to easily train models at scale on sensitive datasets. We also run through example experiments showing how without such approaches we simply cannot train ML models of sufficient quality.

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