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

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NVIDIA's RAPIDS cuML library has been available for years, yet adoption among data science teams remains lower than its performance gains would justify. This MarkTechPost tutorial provides a practical end-to-end walkthrough aimed at practitioners who want to migrate scikit-learn workflows to GPU without a full rewrite. The entry point is cuml.accel, a compatibility layer that accelerates existing scikit-learn code with minimal modifications. From there the tutorial moves to the native cuML API for direct CuPy and cuDF (GPU DataFrame) interoperability. A notable detail: the article addresses synchronized GPU timing as a benchmark requirement, which is a common source of inflated performance claims in GPU tutorials. Beyond standard algorithms, the piece covers GPU implementations of manifold learning (UMAP, t-SNE) and density-based clustering (HDBSCAN, DBSCAN) — workloads where GPU acceleration is less obvious but impactful at scale. The Forest Inference Library (FIL) section shows how to run high-throughput inference on pre-trained tree models without retraining. GPU-generated SHAP explanations and hyperparameter optimization via scikit-learn meta-estimators round out the workflow. The article is tutorial-heavy with full Python code. It is not a product announcement or research publication — its value is as a practical reference for ML engineers who have GPU hardware but have not yet migrated their pipelines.