How NVIDIA Uses LightGBM

This article provides insights into GPU-accelerating machine learning model training using CUDA-X Data Science, focusing on tree-based models like XGBoost, LightGBM, and CatBoost.

The article presents a comprehensive playbook developed through extensive experience in Kaggle competitions, detailing seven effective modeling techniques for handling tabular data.

The article discusses the enhancements in the Forest Inference Library (FIL) within NVIDIA cuML 25. 04, focusing on its capabilities for fast inference of tree-based models.

The article discusses the latest enhancements in RAPIDS, including zero-code-change acceleration for Python machine learning, significant IO performance improvements, and out-of-core XGBoost capabi...

Kaggle Grandmasters David Austin, Chris Deotte, and Ruchi Bhatia shared insights on their winning strategies for data science competitions at the Google Cloud Next conference.

The article discusses the strategies employed by the winners of the NVIDIA hackathon at ODSC West, focusing on how they utilized RAPIDS Python APIs to enhance machine learning workflows.

The article discusses the integration of Metaflow and NVIDIA Triton Inference Server for developing and deploying machine learning models.

The article discusses how GPU-accelerated data analytics can enhance machine learning (ML) projects by improving speed and scalability.

This article discusses the use of time-series models, specifically autoregressive recursive neural networks and XGBoost, for predicting credit defaults.

The article discusses the importance of explainability in machine learning models, particularly through the use of SHAP (SHapley Additive Explanations) and its GPU-accelerated variant, GPUTreeShap.

The article discusses the challenges of deploying AI models in production and how NVIDIA Triton Inference Server addresses these challenges.

The article discusses the deployment of tree-based models like XGBoost and LightGBM using the NVIDIA Triton Inference Server, emphasizing its capabilities for real-time serving and GPU acceleration.

The article discusses the NVIDIA Triton Inference Server, an open-source platform designed for fast and scalable AI model deployment.

The article discusses advancements in AutoML using NVIDIA GPUs and RAPIDS, highlighting how AutoGluon simplifies the process of achieving state-of-the-art machine learning accuracy while significan...

The article discusses the RAPIDS Forest Inference Library (FIL) and its support for sparse tree storage, which significantly reduces memory usage for deep tree-based models.

This article provides an in-depth look at how to leverage machine learning techniques to detect fraud, specifically through the lens of the Kaggle IEEE CIS Fraud Detection competition.