Meetup #119

How Aurora Accelerates Autonomous Vehicle ML Model Development Using Kubeflow

In this talk, team Aurora will discuss how they accelerated ML model development for autonomous vehicles by integrating with Kubeflow. Team Aurora will cover how the Kubeflow infrastructure evolved and how it is currently deployed. Then we will discuss how we build pipelines, the developer experience, and the benefits of using pipelines. Finally, we’ll walk through how they adopt Kubeflow org-wide.

In this episode

Maurizio Vitale

Maurizio Vitale

Staff Engineer, Aurora Innovation

Maurizio joined Aurora when it was still a relatively small company, fewer than 100 people in 2018. He initially worked in the simulation team, helping scaling the infrastructure from a few thousand simulation runs per day to a few millions. Then he joined the Motion Planning team, working on pipelines for feature extraction and more recently the compute team working on MLOps projects, increasing the adoption of kubeflow being the main focus at the moment. In previous lives he wore many hats, from hardware designer, to compiler-like tools, passing by log aggregation for a certain search and advertising company.

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Vinay Anantharaman

Vinay Anantharaman

TLM MLOps, Aurora Innovation

Maurizio joined Aurora when it was still a relatively small company, fewer than 100 people in 2018. He initially worked in the simulation team, helping scaling the infrastructure from a few thousand simulation runs per day to a few millions. Then he joined the Motion Planning team, working on pipelines for feature extraction and more recently the compute team working on MLOps projects, increasing the adoption of kubeflow being the main focus at the moment. In previous lives he wore many hats, from hardware designer, to compiler-like tools, passing by log aggregation for a certain search and advertising company.

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LinkedIn

Ankit Aggarwal

Ankit Aggarwal

Senior Software Engineer, Aurora

Maurizio joined Aurora when it was still a relatively small company, fewer than 100 people in 2018. He initially worked in the simulation team, helping scaling the infrastructure from a few thousand simulation runs per day to a few millions. Then he joined the Motion Planning team, working on pipelines for feature extraction and more recently the compute team working on MLOps projects, increasing the adoption of kubeflow being the main focus at the moment. In previous lives he wore many hats, from hardware designer, to compiler-like tools, passing by log aggregation for a certain search and advertising company.

LinkedIn

Demetrios Brinkmann

Demetrios Brinkmann

Host

Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.

Ben Epstein

Ben Epstein

Host

Ben was the machine learning lead for Splice Machine, leading the development of their MLOps platform and Feature Store. He is now a founding software engineer at Galileo (rungalileo.io) focused on building data discovery and data quality tooling for machine learning teams. Ben also works as an adjunct professor at Washington University in St. Louis teaching concepts in cloud computing and big data analytics.