Autonomous vehicles (AVs) continuously generate and process enormous volumes of sensor data, scaling into the petabyte-per-second regime across large fleets. Efficiently managing, training, and deploying machine learning models at such scale presents unprecedented infrastructure challenges.  This presentation explores cutting-edge strategies and technologies for hyper-scale machine learning training and inference, specifically tailored for AV applications. We delve into system architectures, optimized data management techniques, and compute paradigms that enable efficient training, continuous learning, and real-time inference on vast data streams.

Key Topics Covered: Scalable ML Infrastructure Design: Techniques for parallelization and distributed training across thousands of GPUs and compute nodes.  High-Throughput Data Pipelines: Optimizing data ingestion, preprocessing, labeling, and streaming from AV fleets to centralized data stores.  Resource-Efficient Training: Methods for managing infrastructure resources, minimizing overhead, and maximizing hardware utilization in hyperscale environments.  Real-Time Inference at Scale: Solutions for low-latency inference deployment, edge-to-cloud orchestration, and continuous model updates in large AV fleets.  Case Study – Torc Robotics: Insights from real-world implementations, challenges faced, lessons learned, and future directions based on experiences from Torc Robotics’ core ML team.