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.