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

- NVIDIA dropped a cuML/RAPIDS tutorial showing you can slap `cuml.accel` on your crusty scikit-learn code and get GPU speedups with zero rewrites — PCA, K-Means, random forests, DBSCAN all benchmarked CPU vs GPU. Yes, actual synchronized timing, not vibes-based benchmarks like the average moonboy's "1000x gains" chart. They even validate SHAP explanations on GPU, because speed without explainability is just an unsealed cargo manifest — impressive, unverifiable, and someone's getting billed later. Meanwhile half of crypto Twitter can't explain their own tokenomics, let alone a model. For quant desks still running overnight CPU backtests on threadbare rigs: this is free throughput sitting on the table. Aggressive passive income. Read the tutorial before your competitor does — education is the only alpha regulators can't confiscate.