Machine Learning Models as a Service

Tobias Wenzel and Vigith Maurice, Intuit


"We spent more time bringing the model to production than developing and training it." -Data Scientist, Intuit

The mission of our team at Intuit is to enable data scientists to deploy machine learning models with the push of a button and making them available in production at scale. We will be sharing challenges and solutions on orchestration, monitoring and diagnosing of machine learning models serving production traffic for TurboTax, Quickbooks and Mint.

Tobias Wenzel, Intuit

Tobias Wenzel is a Staff Software Engineer for the Intuit Machine Learning Platform in Mountian 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. He recently moved to the US from Germany to continue his work here in the bay area. Today he focusses on operational excellence of the platform and bringing it successfully through Intuit's seasonal business.

Vigith Maurice, Intuit

Vigith is a Principal Site Reliability Engineer for the Intuit Data Platform team in Mountain View, California. For the past 4 years, he has been a key driver for Intuit's journey to Big Data--first in Intuit data centers, and more recently in the Cloud. One of Vigith's current day-to-day focus areas is on the difficult and various challenges in building scalable monitoring solutions for both batch and high throughput systems. Previously, he lead various engineering initiatives at Yahoo.

Open Access Media

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are posted after the event are also free and open to everyone. Support USENIX and our commitment to Open Access.

@conference {232945,
author = {Tobias Wenzel and Vigith Maurice},
title = {Machine Learning Models as a Service},
year = {2019},
address = {Santa Clara, CA},
publisher = {{USENIX} Association},
month = may,