Implementation of Machine Learning Workflows with NVIDIA cuML, RAPIDS, GPU Benchmarking, Explainability, Clustering, and Model Inference

MarkTechPost · 1 month ago · 4

The tutorial walks through implementing end-to-end machine learning workflows on NVIDIA GPUs using RAPIDS cuML (including cuML.accel for scikit-learn acceleration) plus CuPy/cuDF interoperability, then benchmarking multiple classic ML tasks on CPU versus GPU. Installing cuML in the described setup can take about 1-3 minutes. As a result, you get reproducible GPU benchmarking and inference/explainability pipelines (e.g., clustering and SHAP), with guidance on keeping data on-device to preserve speed and model portability considerations.