As more and more business decisions are made by machine learning models, do you know why your model made a decision? Is it biased? Can you find out which version of your model was in production two months ago? Are you getting alerts if your model is under malicious attack? Is your model performing as it was trained? Is there any concept drift happening?
Harish Doddi and Jerry Xu share the challenges they faced scaling machine learning models and detail the solutions they’re building to conquer them.
Harish Doddi is cofunder and CEO of Datatron. Previously, he held roles at Oracle; Twitter, where he worked on open source technologies, including Apache Cassandra and Apache Hadoop, and built Blobstore, Twitter’s photo storage platform; Snap, where he worked on the backend for Snapchat Stories; and Lyft, where he worked on the surge pricing model. Harish holds a master’s degree in computer science from Stanford, where he focused on systems and databases, and an undergraduate degree in computer science from the International Institute of Information Technology in Hyderabad.
Jerry Xu is cofounder and CTO at Datatron Technologies. An innovative software engineer with extensive programming and design experience in storage systems, online services, mobile, distributed systems, virtualization, and OS kernels, Jerry also has a demonstrated ability to direct and motivate a team of software engineers to complete projects meeting specifications and deadlines. Previously, he worked at Zynga, Twitter, Box, and Lyft, where he built the company’s ETA machine learning model. Jerry is the author of open source project LibCrunch. He’s a three-time Microsoft Gold Star Award winner.
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