Epsilon Software for Private Machine Learning with Chang Liu

    800 800 This Week in Machine Learning & AI

    In this episode, our final episode in the Differential Privacy series, I speak with Chang Liu, applied research scientist at Georgian Partners, a venture capital firm that invests in growth stage business software companies in the US and Canada.

    Chang joined me to discuss Georgian’s new offering, Epsilon, a software product that embodies the research, development and lessons learned helps in helping their portfolio companies deliver differentially private machine learning solutions to their customers. In our conversation, Chang discusses some of the projects that led to the creation of Epsilon, including differentially private machine learning projects at BlueCore, Work Fusion and Integrate.ai. We explore some of the unique challenges of productizing differentially private ML, including business, people and technology issues. Finally, Chang provides some great pointers for those who’d like to further explore this field.

    Thanks to our Sponsor!

    Thanks to Georgian Partners for their continued support of the podcast and for sponsoring this series. Georgian Partners is a venture capital firm that invests in growth stage business software companies in the US and Canada. Post investment, Georgian works closely with portfolio companies to accelerate adoption of key technologies including machine learning and differential privacy. To help their portfolio companies provide privacy guarantees to their customers, Georgian recently launched its first software product, Epsilon, which is a differentially private machine learning solution. You’ll learn more about Epsilon in my interview with Georgian’s Chang Liu later this week, but if you find this field interesting, I’d encourage you to visit the differential privacy resource center they’ve set up at https://gptrs.vc/twimlai

    About Chang

    Mentioned in the Interview

    “More On That Later” by Lee Rosevere licensed under CC By 4.0

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