Ramesh Oswal
Ramesh Oswal is a Senior Motion Planning Engineer at Aurora, with experience from Luminar and Noble.AI. He has expertise in AI/ML for Autonomous Systems and Education. He has also served as a review committee member for NeurIPS 2024, CNCF 2024, and CNCF 2023.
Session
AI/ML workloads depend heavily on complex software stacks, including numerical computing libraries (SciPy, NumPy), deep learning frameworks (PyTorch, TensorFlow), and specialized toolchains (CUDA, cuDNN). However, integrating these dependencies into Bazel-based workflows remains challenging due to compatibility issues, dependency resolution, and performance optimization. This session explores the process of creating and maintaining Bazel packages for key AI/ML libraries, ensuring reproducibility, performance, and ease of use for researchers and engineers.