In this episode of MLOps Live, Sabine and Stephen are joined by Kyle Morris, Co-Founder of Banana ML. They discuss running ML in production leveraging GPUs. They delve into GPU performance optimization, approaches, infrastructural and memory implications as well as other cases. With the increased interest in building production-ready, end-to-end ML pipelines, there’s an increasing need to employ the optimal toolset, which can scale quicker. Modern commodity PCs have a multi-core CPU and at least one GPU, resulting in a low-cost, easily accessible heterogeneous environment for high-performance computing, but due to physical constraints, hardware development now results in greater parallelism rather than improved performance for sequential algorithms. Machine Learning Build/Train and Production Execution frequently employ disparate controls, management, run time platforms, and sometimes languages. As a result, understanding the hardware on which one is running is critical in order to take advantage of any optimization that is feasible.