Uploaded June 2026 | Updated September 2026, 2 weeks ago
Autonomous driving is one of the most demanding multimodal AI problems in industry. Every Torc truck generates terabytes of time-synced data per day. And turning that raw sensor data into shipped autonomy capability requires running perception training, autolabeling, metadata processing, simulation, and test evaluation at scale.
In this session, Neil Wadhvana, Staff ML Engineer at Torc Robotics, will walk through how Torc consolidated its autonomy data processing stack to support multimodal AI at scale with Ray on Anyscale. He will cover:
The trends driving growth in autonomous driving developments,
An overview of Torc’s data loop from production to consumption,
The internal trends in multimodal AI that drove need for consolidation,
The before and after Ray was adopted as common compute framework.
If you're a perception engineer, ML engineer, or autonomy infrastructure engineer working on AVs, ADAS, robotics, or any physical AI system, this session is for you.
Autonomous driving is one of the most demanding multimodal AI problems in industry. Every Torc truck generates terabytes of time-synced data per day. And turning that raw sensor data into shipped autonomy capability requires running perception training, autolabeling, metadata processing, simulation, and test evaluation at scale.
In this session, Neil Wadhvana, Staff ML Engineer at Torc Robotics, will walk through how Torc consolidated its autonomy data processing stack to support multimodal AI at scale with Ray on Anyscale. He will cover:
The trends driving growth in autonomous driving developments,
An overview of Torc’s data loop from production to consumption,
The internal trends in multimodal AI that drove need for consolidation,
The before and after Ray was adopted as common compute framework.
If you're a perception engineer, ML engineer, or autonomy infrastructure engineer working on AVs, ADAS, robotics, or any physical AI system, this session is for you.
