Kevin Lee
Kevin Lee is a senior technical content developer on the Deep Learning Institute Team at NVIDIA. Kevin’s work focuses on raising awareness and driving adoption for GPU-accelerated technologies by creating developer focused hands-on training with an emphasis on Data Science, Computer Vision, and Large Language Models. Prior to NVIDIA, Kevin led a risk analytics team at Morgan Stanley and taught Data Science and Machine Learning at the University of California, Berkeley.
Sessions
As data science continues to evolve, the ever-growing size of datasets poses significant computational challenges. Traditional CPU-based processing often struggles to keep pace with the demands of data science workflows. Accelerated computing with GPUs offers a solution by enabling massive parallelism and significantly reducing processing times for data-heavy tasks. In this session, we will explore GPU computing architecture, how it differs from CPUs, and why it is particularly well-suited for data science workloads. This hands-on lab will dive into the different approaches to GPU programming, from low-level CUDA coding to high-level Python libraries within RAPIDS such as, CuPy, cuDF, cuGraph, and cuML.