This post is the first installment of the series of introductions to the RAPIDS ecosystem. The series explores and discusses various aspects of RAPIDS that allow its users solve ETL (Extract, Transform, Load) problems, build ML (Machine Learning) and DL (Deep Learning) models, explore expansive graphs, process geospatial, signal, and system log data, or use SQL language via BlazingSQL to process data.
|Pandas DataFrame Tutorial – Beginner’s Guide to GPU Accelerated
GPU-accelerated computing is a game-changer for large-scale analytics and data processing. RAPIDS makes leveraging GPUs easy by abstracting the complexities of accelerated data science through familiar interfaces.