Data science demands the interactive exploration of large volumes of data, combined with computationally intensive algorithms and analytics. Today, the computational limits of CPUs are being realized, and a new approach is needed.
In this talk, we will discuss how GPUs can enable data scientists to perform feature engineering and train machine learning models at scale using RAPIDS
Akshit is a deep learning solutions architect at NVIDIA focused on deploying machine learning and deep learning platforms at scale. As an architect, he helps accelerate deep learning pipelines using NVIDIA GPUs at various tech companies. Previously at CU Boulder, he developed deep learning models to understand how students learn on an online learning platform. His work also includes predicting weather using LSTMs and automatically completing a painting in virtual reality using sketch-RNN. He is interested in creative applications of machine learning/deep learning and the wide set of possibilities it presents.