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Published Apr 9, 2020

Credit Check: Capital One’s Kyle Nicholson on Modern Machine Learning in Finance - Ep. 114

Kyle Nicholson from Capital One delves into the transformative role of modern machine learning in finance, emphasizing the efficiency gains, open source integration, and regulatory challenges involved in scaling data science within a corporate setting.
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  • Compliance

    Navigating the regulatory compliance process at Capital One involves a meticulous approach to open source contributions. explains that contributors must undergo a series of legal and cybersecurity reviews before gaining trusted contributor status. This status allows them to contribute across multiple repositories, streamlining the process significantly.

    With large projects like dask and Rapids, where they work over many repositories, I think dask, I mean, dask has a couple tens, 1520 different repositories that can take a long time to get all of the approvals for this.

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    This governance policy ensures that contributions are secure and compliant, facilitating quicker integration into public code bases 1.

       

    Restrictions

    Enterprise restrictions at Capital One present unique challenges for open source contributions. Kyle notes that the company's AWS environment is heavily restricted, limiting how packages can be installed. This often conflicts with the assumptions made by open source developers, who typically have fewer constraints.

    At Capital one, our AWS environment is pretty restricted for good reason, and we're not able to install packages in certain ways.

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    Despite these challenges, Capital One has developed internal checks that allow for more efficient contributions while maintaining security 1.

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