How Aurora Accelerates Autonomous Vehicle ML Model Development Using Kubeflow

Ankit Aggarwal is Senior Software Engineer at Aurora. He has worked in the ML Platform space for over 6 years. Most recently, he has been leading the effort to accelerate the autonomy workflows at Aurora with Kubeflow Pipelines. His earlier work included building ML Inference as a Service platform for large scale metrics and simulation at Uber ATG and contributions to several open-source projects.

Ankit has a MS degree in CS from Stony Brook University and a BE degree from NSIT, India. Outside of work, he enjoys running, tennis, music, art and cinema.

Technical Lead and manager of MLOps at Aurora. He currently develops the infrastructure for ML Training and large scale model inference. Previously he founded a computer vision startup focused on food recognition for consumers. He built a mobile applications utilizing computer vision and the backend systems for model inference.

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.