Srivathsan Canchi and Tobias Wenzel, Intuit Inc.
At Intuit, machine learning models are derived from huge, sensitive data sets that are continuously evolving, which in turn requires continuous model training and tuning with a high level of security and compliance. Intuit’s Machine Learning Platform provides Model LifeCycle management capabilities that are scalable and secure using GitOps, SageMaker, Kubernetes and Argo Workflows.
In this talk, we’ll go over the model management problem statement at Intuit, data science/MLE needs vs Intuit’s enterprise needs, provide an introduction to our model management interface and self serve capabilities. This talk will cover aspects of our platform such as feature management and processing, bill backs, collaborations and separation of operational concerns between platform and model. These capabilities of the platform have enabled model publishing velocity increases of over 200%, and this talk will illustrate how we got there.
Srivathsan Canchi, Intuit Inc.
Srivathsan Canchi leads the machine learning platform engineering team at Intuit. The ML platform includes real-time distributed featurization, scoring and feedback loops. He has a breadth of experience building high scale mission critical platforms. Srivathsan also has extensive experience with K8s at Intuit and previously at eBay, where his team was responsible for building a PaaS on top of K8s and OpenStack.
Tobias Wenzel, Intuit Inc.
Tobias Wenzel is a Software Engineer for the Intuit Machine Learning Platform in Mountain View, California. He has been working on the platform since its inception in 2016 and has helped design and build it from the ground up. In his job he has focused on operational excellence of the platform and bringing it successfully through Intuit's seasonal business. In addition, he is passionate about continuously expanding the platform with the latest technologies.
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author = {Srivathsan Canchi and Tobias Wenzel},
title = {Managing {ML} Models @ Scale - {Intuit{\textquoteright}s} {ML} Platform},
year = {2020},
publisher = {USENIX Association},
month = jul
}