cuML

Jul 17, 2025
Feature Engineering at Scale: Optimizing?ML Models in Semiconductor Manufacturing with NVIDIA?CUDA?X?Data Science
In our previous post, we introduced the setup of predictive modeling in chip manufacturing and operations, highlighting common challenges such as imbalanced...
6 MIN READ

Jun 18, 2025
AI in Manufacturing and Operations at NVIDIA: Accelerating ML Models with NVIDIA CUDA-X Data Science
NVIDIA leverages data science and machine learning to optimize chip manufacturing and operations workflows—from wafer fabrication and circuit probing to...
8 MIN READ

Jun 05, 2025
Supercharge Tree-Based Model Inference with Forest Inference Library in NVIDIA cuML
Tree-ensemble models remain a go-to for tabular data because they're accurate, comparatively inexpensive to train, and fast. But deploying Python inference on...
11 MIN READ

May 22, 2025
Grandmaster Pro Tip: Winning First Place in a Kaggle Competition with Stacking Using cuML
What does it take to win a Kaggle competition in 2025? In the April Playground challenge, the goal was to predict how long users would listen to a podcast—and...
7 MIN READ

May 08, 2025
Spotlight: Accelerating the Discovery of New Battery Materials with SES AI's Molecular Universe
From the Stone Age to the digital era, materials have been the foundation of our civilization across all epochs. Today, finding new materials leads to progress...
7 MIN READ

May 07, 2025
Building Nemotron-CC, A High-Quality Trillion Token Dataset for LLM Pretraining from Common Crawl Using NVIDIA NeMo Curator
Curating high-quality pretraining datasets is critical for enterprise developers aiming to train state-of-the-art large language models (LLMs). To enable...
7 MIN READ

May 01, 2025
Stacking Generalization with HPO: Maximize Accuracy in 15 Minutes with NVIDIA cuML
Stacking generalization is a widely used technique among machine learning (ML) engineers, where multiple models are combined to boost overall predictive...
7 MIN READ

Mar 18, 2025
NVIDIA cuML Brings Zero Code Change Acceleration to scikit-learn
Scikit-learn, the most widely used ML library, is popular for processing tabular data because of its simple API, diversity of algorithms, and compatibility with...
8 MIN READ

Jan 16, 2025
Accelerating Time Series Forecasting with RAPIDS cuML
Time series forecasting is a powerful data science technique used to predict future values based on data points from the past Open source Python libraries like...
4 MIN READ

Dec 12, 2024
Harnessing GPU Acceleration for Multi-Label Classification with RAPIDS cuML
Modern classification workflows often require classifying individual records and data points into multiple categories instead of just assigning a single label....
4 MIN READ

Nov 14, 2024
Faster Causal Inference on Large Datasets with NVIDIA RAPIDS
As consumer applications generate more data than ever before, enterprises are turning to causal inference methods for observational data to help shed light on...
4 MIN READ

Oct 31, 2024
Even Faster and More Scalable UMAP on the GPU with RAPIDS cuML
UMAP is a popular dimension reduction algorithm used in fields like bioinformatics, NLP topic modeling, and ML preprocessing. It works by creating a k-nearest...
12 MIN READ

Aug 29, 2024
Just Released: RAPIDS 24.08
RAPIDS 24.08 is now available with significant updates geared towards processing larger workloads and seamless CPU/GPU interoperability.
1 MIN READ

Oct 13, 2023
Supercharge Graph Analytics at Scale with GPU-CPU Fusion for 100x Performance
Graphs form the foundation of many modern data and analytics capabilities to find relationships between people, places, things, events, and locations across...
11 MIN READ

Aug 30, 2023
Workshop: Enhancing Data Science Outcomes with Efficient Workflows
Learn to create an end-to-end machine learning pipeline for large datasets with this virtual, hands-on workshop.
1 MIN READ

Aug 23, 2023
ICYMI: Utilizing GPUs for Machine Learning with RAPIDS
Delve into how TMA Solutions is accelerating original ML and AI workflows with RAPIDS.
1 MIN READ