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2. [Data Science](/topics/ai/data-science)

CUDA-X Data Science Libraries

# CUDA-X for Data Science

CUDA-X™ is a [collection of highly optimized, domain-specific libraries built on CUDA](https://www.nvidia.com/en-us/technologies/cuda-x/)™ that includes a suite of open source libraries for accelerated data science. With 100+ integrations with open source libraries and tools in the data science ecosystem and zero-code-change APIs that accelerate popular PyData tools like pandas and scikit-learn, data scientists can easily accelerate their workflows with their existing tools.

[Download Now](https://docs.rapids.ai/install?_gl=1*kwbd1w*_ga*MTE4NDAwMTQ1NS4xNzA5NzcwODcw*_ga_RKXFW6CM42*czE3NTIxODk0OTQkbzk1JGcwJHQxNzUyMTg5NDk0JGo2MCRsMCRoMA)[Documentation](https://docs.rapids.ai/)

 ![NVIDIA CUDA-X Data Science open-source libraries](https://developer.download.nvidia.com/images/cuda-x/cuda-x-for-data-science.svg)
* * *

## CUDA-X Libraries for Data Science 

CUDA-X libraries accelerate data and graph analytics, machine learning, and data visualizations. Data scientists can optimize for performance on single GPUs or scale up to distributed systems.

### cuDF: 20x Faster Polars

cuDF is a toolkit that contains GPU-accelerated libraries to optimize fundamental DataFrame operations. It includes drop-in accelerators for popular DataFrame libraries and SQL engines, like Polars, pandas, and Apache Spark with no code changes required.

[Learn More About cuDF](/cudf)

[View Docs](https://docs.rapids.ai/api/cudf/stable/)

**TAGS: pandas, polars, apache spark, dataframe, Python, C++**

### cuML: 50x Faster Scikit-learn

cuML is a GPU-accelerated machine learning library that optimizes machine learning algorithms for execution on GPUs. It includes accelerators that run machine learning algorithms in scikit-learn, UMAP, and HDBSCAN with no code changes required. 

[Learn More About cuML](/cuml)

[View Docs](https://docs.rapids.ai/api/cuml/stable/)

**TAGS: scikit-learn, machine learning, Python, C++**

### cuGraph: 48x Faster NetworkX

cuGraph is a GPU-accelerated graph analytics library that optimizes graph algorithms for execution on GPUs to process millions of nodes without specialized software. It includes a zero-code-change accelerator for NetworkX. 

[View Docs](https://docs.rapids.ai/api/cugraph/stable/)

**TAGS: NetworkX, graph, Python, C++**

### cuxfilter

Create interactive data visuals with multidimensional filtering of over 100-million-row tabular datasets.

[Get Started With cuxfilter](https://docs.rapids.ai/api/cuxfilter/stable/)

**Tags: dashboards, visualization, Python**

### Dask

Scale out GPU-accelerated data science pipelines for machine learning, XGBoost, and graph analytics to multiple nodes on Dask.

[Get Started on GitHub](https://github.com/rapidsai/cudf/tree/main/python/dask_cudf)

**Tags: distributed computing, Python**

### Apache Spark

Accelerate Apache Spark data processing workflows on NVIDIA GPUs with the RAPIDS™ Accelerator for Apache Spark.

[Get Started With Spark](https://nvidia.github.io/spark-rapids/)

**TAGS: distributed computing, data processing, Python**

### Other CUDA-X Libraries for Data Science 

See a complete list of libraries and tools.

[Check Out GitHub](https://github.com/rapidsai)

 

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## Install and Deploy in Your Environment

Quick Install

Deployment Guides

### Quick Install With conda

1. If not installed, download and run the install script. This will install the latest miniforge:

    wget "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh" bash Miniforge3-$(uname)-$(uname -m).sh

2. Then install with:

    conda create -n rapids-26.06 -c rapidsai -c conda-forge rapids=26.06 python=3.14 'cuda-version\>=13.0,\<=13.2'

### Quick Install With pip

    pip install \ --extra-index-url=https://pypi.nvidia.com \ "cudf-cu13==26.6.\*" \ "dask-cudf-cu13==26.6.\*" \ "cuml-cu13==26.6.\*" \ "cugraph-cu13==26.6.\*"

### Deploy Locally

Use this guide to install and build with conda, pip, Docker, or WSL2 on your local machine.

[Read the Local Deployment Guide](https://docs.rapids.ai/deployment/stable/local/)

### Deploy on Platforms  

Deploy on your platform of choice, including Kubernetes, Databricks, and Google Colab.

[Read the Platforms Guide](https://docs.rapids.ai/deployment/stable/platforms/)

### Deploy in the Cloud

Run in AWS, Azure, GCP, and more.

[Read the Cloud Deployment Guide](https://docs.rapids.ai/deployment/stable/cloud/)

* * *

## Data Science Learning Library



| title | featured | x_formats | document_url | technologies | document_date | short_summary | document_title | learning_level | x_content_types |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Merlin Tensorflow Inference | false | webpage | https://catalog.ngc.nvidia.com/orgs/nvidia/teams/merlin/containers/merlin-tensorflow-inference | Merlin | 2025-11-06T19:13:22.000Z | Deploy NVTabular workflows and TensorFlow recommender models to Triton Inference Server for production. | Merlin Tensorflow Inference | Technical - Beginner | Documentation |
| Merlin Tensorflow Training | false | webpage | https://catalog.ngc.nvidia.com/orgs/nvidia/teams/merlin/containers/merlin-tensorflow-training | Merlin | 2025-11-06T19:13:23.000Z | Reference the Merlin container to preprocess with NVTabular and train TensorFlow recommender models. | Merlin Tensorflow Training | Technical - Beginner | Documentation |
| NVIDIA Morpheus PB May 2024 (PB 24h1) | false | webpage | https://catalog.ngc.nvidia.com/orgs/nvidia/containers/morpheus-pb24h1 | Morpheus | 2025-11-06T15:50:36.000Z | Reference Morpheus Production Branch 24h1 for API-stable cybersecurity deployments with monthly security patches. | NVIDIA Morpheus PB May 2024 (PB 24h1) | Technical - Beginner | Documentation |
| Merlin PyTorch Inference | false | webpage | https://catalog.ngc.nvidia.com/orgs/nvidia/teams/merlin/containers/merlin-pytorch-inference | Merlin | 2025-11-06T19:13:20.000Z | Deploy NVTabular workflows and PyTorch recommender models to Triton Inference Server for production. | Merlin PyTorch Inference | Technical - Beginner | Documentation |
| Merlin PyTorch Training | false | webpage | https://catalog.ngc.nvidia.com/orgs/nvidia/teams/merlin/containers/merlin-pytorch-training | Merlin | 2025-11-06T19:13:21.000Z | Reference the Merlin container to preprocess data with NVTabular and train PyTorch recommender models. | Merlin PyTorch Training | Technical - Beginner | Documentation |
| MLflow Triton Plugin | false | webpage | https://catalog.ngc.nvidia.com/orgs/nvidia/teams/morpheus/containers/mlflow-triton-plugin | Morpheus | 2026-01-21T16:17:09.000Z | Reference the MLflow Triton plugin to deploy MLflow-registered models to Triton Inference Server. | MLflow Triton Plugin | Technical - Beginner | Documentation |
| Merlin TensorFlow | false | webpage | https://catalog.ngc.nvidia.com/orgs/nvidia/teams/merlin/containers/merlin-tensorflow | Merlin | 2025-12-11T19:37:21.000Z | Reference the Merlin TensorFlow container for preprocessing, training, and serving recommender models. | Merlin TensorFlow | Technical - Beginner | Documentation |
| Merlin PyTorch | false | webpage | https://catalog.ngc.nvidia.com/orgs/nvidia/teams/merlin/containers/merlin-pytorch | Merlin | 2025-12-11T19:36:16.000Z | Reference the Merlin container to train PyTorch recommenders with NVTabular and serve via Triton. | Merlin PyTorch | Technical - Beginner | Documentation |
| Merlin HugeCTR | false | webpage | https://catalog.ngc.nvidia.com/orgs/nvidia/teams/merlin/containers/merlin-hugectr | Merlin | 2025-12-11T19:35:39.000Z | Preprocess data, engineer features, and train large-scale recommender models with Merlin HugeCTR. | Merlin HugeCTR | Technical - Beginner | Documentation |
| Morpheus | false | webpage | https://catalog.ngc.nvidia.com/orgs/nvidia/teams/morpheus/containers/morpheus | Morpheus | 2026-01-21T17:13:07.000Z | Reference Morpheus, an open AI application framework for building cybersecurity solutions. | Morpheus | Technical - Beginner | Documentation |
| cuOpt | false | webpage | https://catalog.ngc.nvidia.com/orgs/nvidia/teams/cuopt/containers/cuopt | cuOpt | 2026-02-12T00:16:34.000Z | Reference the cuOpt container to run GPU-accelerated decision optimization at million-variable scale. | cuOpt | Technical - Beginner | Documentation |
| Accelerate Decision Optimization Using Open Source NVIDIA cuOpt | false | blog | https://developer.nvidia.com/blog/accelerate-decision-optimization-using-open-source-nvidia-cuopt/ | cuOpt | 2025-06-11T04:00:00.000Z | Explore how open-source NVIDIA cuOpt accelerates large-scale decision optimization across production, logistics, and allocation. | Accelerate Decision Optimization Using Open Source NVIDIA cuOpt | Technical - Beginner | Explainer |
| Enhance Generative AI Model Accuracy Through Data Processing | false | webpage | https://www.nvidia.com/en-us/events/enhance-generative-ai-model-accuracy/ | NeMo | 2026-02-17T10:12:00.000Z | Learn how NeMo Curator builds scalable pipelines creating high-quality multimodal datasets for generative AI. | Enhance Generative AI Model Accuracy Through Data Processing | Technical - Beginner | Documentation |
| NIM Agent Blueprint for Vulnerability Analysis | false | webpage | https://catalog.ngc.nvidia.com/orgs/nvidia/teams/morpheus/containers/morpheus-vuln-analysis | Morpheus | 2026-01-21T16:17:11.000Z | Deploy the Morpheus NIM Agent Blueprint to accelerate container vulnerability detection with generative AI. | NIM Agent Blueprint for Vulnerability Analysis | Technical - Beginner | Documentation |
| Morpheus Triton Server Models | false | webpage | https://catalog.ngc.nvidia.com/orgs/nvidia/teams/morpheus/containers/morpheus-tritonserver-models | Morpheus | 2026-01-21T16:17:07.000Z | Reference pretrained Morpheus cybersecurity models packaged on Triton Inference Server for rapid deployment. | Morpheus Triton Server Models | Technical - Beginner | Documentation |
| NVIDIA Morpheus PB October 2024 (PB 24h2) | false | webpage | https://catalog.ngc.nvidia.com/orgs/nvidia/containers/morpheus-pb24h2 | Morpheus | 2025-11-06T15:50:36.000Z | Reference Morpheus Production Branch 24h2 for API-stable cybersecurity deployments with monthly security patches. | NVIDIA Morpheus PB October 2024 (PB 24h2) | Technical - Beginner | Documentation |
| NVIDIA Retrieval QA E5 Embedding Model | false | hands-on | https://build.nvidia.com/nvidia/nv-embedqa-e5-v5 | NeMo Retriever | 2026-02-17T10:12:00.000Z | Experience an English text embedding model optimized for question-answering retrieval. | NVIDIA Retrieval QA E5 Embedding Model | Technical - Beginner | Demo |
| Merlin Training | false | webpage | https://catalog.ngc.nvidia.com/orgs/nvidia/teams/merlin/containers/merlin-training | Merlin | 2025-11-06T19:13:24.000Z | Reference the Merlin container to preprocess with NVTabular and train HugeCTR recommender models. | Merlin Training | Technical - Beginner | Documentation |
| cuOpt | false | webpage | https://catalog.ngc.nvidia.com/orgs/nvidia/collections/cuopt | cuOpt | 2026-01-26T17:11:17.000Z | Consult the cuOpt collection to access GPU-accelerated decision optimization resources and containers. | cuOpt | Technical - Beginner | Documentation |
| NVIDIA NIMs for Retrieval | false | hands-on | https://build.nvidia.com/explore/retrieval | NIM | 2026-02-17T10:12:00.000Z | Experience NVIDIA retrieval NIMs to connect LLMs with proprietary enterprise data. | NVIDIA NIMs for Retrieval | Technical - Beginner | Demo |
| NVIDIA cuOpt on GitHub | false | code | https://github.com/NVIDIA/cuopt | cuOpt | 2026-02-17T10:12:00.000Z | Study and fork cuOpt to accelerate LP, MIP, and VRP optimization on GPUs. | NVIDIA cuOpt on GitHub | Technical - Intermediate | Samples |
| Solve Linear Programs Using the GPU-Accelerated Barrier Method in NVIDIA cuOpt | false | blog | https://developer.nvidia.com/blog/solve-linear-programs-using-the-gpu-accelerated-barrier-method-in-nvidia-cuopt/ | cuOpt | 2025-10-24T09:00:00.000Z | Explore how cuOpt's GPU-accelerated barrier method solves large-scale linear programming problems efficiently. | Solve Linear Programs Using the GPU-Accelerated Barrier Method in NVIDIA cuOpt | Technical - Advanced | Explainer |
| Develop Text Retrieval Pipelines for RAG | false | blog | https://developer.nvidia.com/blog/develop-production-grade-text-retrieval-pipelines-for-rag-with-nvidia-nemo-retriever | NeMo Retriever | 2026-02-17T10:12:00.000Z | Learn how to build production-grade text retrieval pipelines for RAG using NeMo Retriever NIMs. | Develop Text Retrieval Pipelines for RAG | Technical - Intermediate | Explainer |
| NVIDIA Retrieval QA Mistral 4B Reranking Model | false | hands-on | https://build.nvidia.com/nvidia/nv-rerankqa-mistral-4b-v3/modelcard | NeMo Retriever | 2026-02-17T10:12:00.000Z | Reference a Mistral 4B reranker that scores document relevance to QA queries. | NVIDIA Retrieval QA Mistral 4B Reranking Model | Technical - Beginner | Documentation |
| NVIDIA Retrieval QA Mistral 7B Embedding Model | false | hands-on | https://build.nvidia.com/nvidia/nv-embedqa-mistral-7b-v2 | NeMo Retriever | 2026-02-17T10:12:00.000Z | Experience a multilingual embedding model that transforms text into dense vectors for QA retrieval. | NVIDIA Retrieval QA Mistral 7B Embedding Model | Technical - Beginner | Demo |
| NVIDIA NIM for Retrieval Documentation | false | webpage | https://docs.nvidia.com/nim/index.html#nemo-retriever | NeMo Retriever, NIM | 2026-02-17T10:12:00.000Z | Build retrieval pipelines with NeMo Retriever NIM microservices for text embedding and reranking. | NVIDIA NIM for Retrieval Documentation | Technical - Intermediate | Tutorial |
| NVIDIA Morpheus PB May 2025 (PB 25h1) | false | webpage | https://catalog.ngc.nvidia.com/orgs/nvidia/containers/morpheus-pb25h1 | Morpheus | 2025-11-25T18:41:12.000Z | Deploy the Morpheus Production Branch container offering nine-month API stability for enterprise cybersecurity. | NVIDIA Morpheus PB May 2025 (PB 25h1) | Technical - Beginner | Documentation |
| Use a Route Optimization Cloud Service | false | video | https://www.nvidia.com/en-us/on-demand/session/gtc24-dlit62051/ | cuOpt | 2026-02-17T10:12:00.000Z | Learn how to use the cuOpt cloud service to optimize heterogeneous-fleet vehicle routing. | Use a Route Optimization Cloud Service | Technical - Intermediate | Explainer |
| Snowflake Arctic Embedding Models | false | hands-on | https://build.nvidia.com/snowflake/arctic-embed-l | NeMo Retriever | 2026-02-17T10:12:00.000Z | Experience Snowflake Arctic Embed L, an optimized text embedding model delivering high-quality retrieval performance. | Snowflake Arctic Embedding Models | Technical - Beginner | Demo |
| Cybersecurity Developer Day | false | video | https://www.nvidia.com/en-us/on-demand/session/gtc24-se62821/ | Morpheus | 2026-02-17T10:08:39.000Z | Learn how LLMs, generative AI, RAG, and ML address modern cybersecurity challenges with Morpheus. | Cybersecurity Developer Day | Technical - Beginner | Explainer |
| NV-Embed Model | false | hands-on | https://build.nvidia.com/nvidia/nv-embed-v1/modelcard | NeMo Retriever | 2026-02-17T10:12:00.000Z | Reference NV-Embed, a generalist embedding model excelling across retrieval, reranking, classification, and clustering tasks. | NV-Embed Model | Technical - Beginner | Documentation |
| Learn How NVIDIA cuOpt Accelerates Mixed Integer Optimization using Primal Heuristics | false | blog | https://developer.nvidia.com/blog/learn-how-nvidia-cuopt-accelerates-mixed-integer-optimization-using-primal-heuristics/ | cuOpt | 2026-01-13T12:32:44.000Z | Examine how NVIDIA cuOpt uses GPU primal heuristics to accelerate mixed-integer optimization. | Learn How NVIDIA cuOpt Accelerates Mixed Integer Optimization using Primal Heuristics | Technical - Advanced | Explainer |
| NIM Agent Blueprint for Vulnerability Analysis | false | webpage | https://catalog.ngc.nvidia.com/orgs/nvidia/teams/morpheus/collections/vulnerability_analysis | Morpheus | 2025-03-14T06:27:57.000Z | Accelerate container vulnerability triage using the Morpheus NIM Agent Blueprint for security analysis. | NIM Agent Blueprint for Vulnerability Analysis | Technical - Beginner | Documentation |
| Retriever Benchmark Tool | false | webpage | https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo-microservices/containers/eval-tool-benchmark-retriever | NeMo Retriever | 2025-11-06T14:16:18.000Z | Reference the NeMo evaluation benchmark container to measure retrieval system performance. | Retriever Benchmark Tool | Technical - Beginner | Documentation |
| NVIDIA Retriever and RAG Eval | false | webpage | https://catalog.ngc.nvidia.com/orgs/nvidia/teams/eval-factory/containers/rag_retriever_eval | NeMo Retriever | 2026-02-02T18:08:11.000Z | Reference the NeMo Evaluator container to benchmark retriever and RAG pipelines. | NVIDIA Retriever and RAG Eval | Technical - Beginner | Documentation |
| Generative AI Examples | false | code | https://github.com/NVIDIA/GenerativeAIExamples | Dynamo, TensorRT, Triton Inference Server | 2026-02-17T10:12:00.000Z | Fork generative AI reference workflows optimized for accelerated infrastructure and microservice architecture. | Generative AI Examples | Technical - Intermediate | Samples |
| Talk to Your Supply Chain Data Using NIM | false | video | https://www.youtube.com/watch?v=a9O0JipIrb4 | cuOpt | 2026-02-17T10:08:38.000Z | Examine how NVIDIA accelerates supply chain decision-making using GPU-powered optimization. | Talk to Your Supply Chain Data Using NIM | Technical - Beginner | Explainer |
| NVIDIA cuOpt on Microsoft Azure Marketplace | false | video | https://www.youtube.com/watch?v=W7-jMYp58rc | cuOpt | 2026-02-17T10:12:00.000Z | Deploy NVIDIA cuOpt from the Microsoft Azure Marketplace in three straightforward steps. | NVIDIA cuOpt on Microsoft Azure Marketplace | Technical - Beginner | Tutorial |
| Getting Started with Morpheus | false | webpage | https://docs.nvidia.com/morpheus/getting_started.html | Morpheus | 2026-02-17T10:08:39.000Z | Implement real-time AI cybersecurity pipelines by installing, configuring, and running Morpheus end-to-end. | Getting Started with Morpheus | Technical - Beginner | Tutorial |
| Optimized Vehicle Routing | false | course | https://courses.nvidia.com/courses/course-v1:DLI+T-FX-05+V1/ | cuOpt | 2026-02-17T10:12:00.000Z | Practice preprocessing routing data and composing cuOpt problem variants in this DLI course. | Optimized Vehicle Routing | Technical - Intermediate | Tutorial |
| NVIDIA cuOpt on AWS Marketplace | false | video | https://www.youtube.com/watch?v=qAyQZmhpEgg | cuOpt | 2026-02-17T10:12:00.000Z | Practice launching NVIDIA cuOpt from AWS Marketplace in three steps using the prebuilt VMI. | NVIDIA cuOpt on AWS Marketplace | Technical - Beginner | Tutorial |
| SDS 885: Python Polars: The Definitive Guide, with Jeroen Janssens and Thijs Nieuwdorp - SuperDataScience \| Machine Learning \| AI \| Data Science Career \| Analytics \| Success | false | webpage | https://superdatascience.com/podcast/sds-885-python-polars-the-definitive-guide-with-jeroen-janssens-and-thijs-nieuwdorp | cuDF | 2025-05-06T00:00:00.000Z | Learn how Python Polars cuts memory and compute up to 10x versus pandas. | SDS 885: Python Polars: The Definitive Guide, with Jeroen Janssens and Thijs Nieuwdorp - SuperDataScience \| Machine Learning \| AI \| Data Science Career \| Analytics \| Success | Technical - Intermediate | Explainer |
| NVIDIA RAPIDS and Open Source ML Acceleration with Chris Deotte and Jean-Francois Puget - Software Engineering Daily | false | webpage | https://softwareengineeringdaily.com/2025/03/04/nvidia-rapids-and-open-source-ml-acceleration-with-chris-deotte-and-jean-francois-puget | cuML | 2025-03-04T10:00:39.000Z | Learn how RAPIDS accelerates open-source machine learning across pandas, Polars, scikit-learn, and NetworkX. | NVIDIA RAPIDS and Open Source ML Acceleration with Chris Deotte and Jean-Francois Puget - Software Engineering Daily | Technical - Intermediate | Explainer |
| Episode #516 - Accelerating Python Data Science at NVIDIA \| Talk Python To Me Podcast | false | webpage | https://talkpython.fm/episodes/show/516/accelerating-python-data-science-at-nvidia | cuDF, cuGraph, cuML | 2025-08-19T04:00:00.000Z | Learn how RAPIDS delivers GPU acceleration for pandas, scikit-learn, Polars, and NetworkX workflows. | Episode #516 - Accelerating Python Data Science at NVIDIA \| Talk Python To Me Podcast | Technical - Beginner | Explainer |
| Supercharging Machine Learning in Snowflake with NVIDIA CUDA-X Libraries for Scikit-learn and Pandas | false | blog | https://snowflake.com/en/blog/nvidia-gpu-acceleration | cuDF, cuML | 2025-11-17T23:47:18.000Z | Explore how Snowflake integrates NVIDIA GPU acceleration to speed up data science workloads. | Supercharging Machine Learning in Snowflake with NVIDIA CUDA-X Libraries for Scikit-learn and Pandas | Technical - Beginner | Explainer |
| Mapping Drug Response Cascades with a Causal Gene Regulatory Network at 100M-Cell Scale | false | blog | https://blog.tahoebio.ai/p/mapping-drug-response-cascades-with | cuML | 2026-03-16T20:46:10.000Z | Explore GPU-accelerated causal gene regulatory networks that map drug response at 100M-cell scale. | Mapping Drug Response Cascades with a Causal Gene Regulatory Network at 100M-Cell Scale | Technical - Intermediate | Explainer |
| Episode #509 - GPU Programming in Pure Python \| Talk Python To Me Podcast | false | webpage | https://talkpython.fm/episodes/show/509/gpu-programming-in-pure-python | cuDF | 2025-06-11T04:00:00.000Z | Explore how Python SDKs deliver near-native GPU performance for data science and ML workloads. | Episode #509 - GPU Programming in Pure Python \| Talk Python To Me Podcast | Technical - Intermediate | Explainer |
| #286 Data Science Trends From 2 Kaggle Grandmasters | false | webpage | https://datacamp.com/podcast/data-science-trends-from-2-kaggle-grandmasters | cuML | 2025-02-24T05:00:00.000Z | Examine AI agents, GPU acceleration, and competitive trends reshaping modern data science practice. | #286 Data Science Trends From 2 Kaggle Grandmasters | Technical - Intermediate | Explainer |
| GraphGeeks In Discussion: RAPIDS and cuGraph with NVIDIA's Joe Eaton | false | video | https://youtube.com/watch?v=kNrkHWjZaeM | cuGraph | 2025-03-03T05:00:00.000Z | Explore how RAPIDS and cuGraph accelerate graph analytics on GPUs for scalable data science. | GraphGeeks In Discussion: RAPIDS and cuGraph with NVIDIA's Joe Eaton | Technical - Intermediate | Explainer |
| #304 Accelerating Data Science with Nick Becker & Dan Hannah | false | webpage | https://datacamp.com/podcast/accelerating-data-science | cuML | 2025-07-01T04:00:00.000Z | Examine GPU-accelerated data science applied to battery discovery and materials research. | #304 Accelerating Data Science with Nick Becker & Dan Hannah | Technical - Intermediate | Explainer |
| Polars — GPU acceleration with Polars and NVIDIA RAPIDS | false | blog | https://pola.rs/posts/gpu-engine-release | cuDF | 2025-01-01T05:00:00.000Z | Explore how the Polars GPU engine uses RAPIDS to accelerate DataFrame queries. | Polars — GPU acceleration with Polars and NVIDIA RAPIDS | Technical - Beginner | Explainer |
| Accelerating the Future of Transportation with SES AI’s NVIDIA-Powered Innovation for Electric Vehicles | false | blog | https://developer.nvidia.com/blog/accelerating-the-future-of-transportation-with-ses-ais-nvidia-powered-innovation-for-electric-vehicles | cuML | 2025-03-25T13:00:00.000Z | Examine how SES AI uses NVIDIA accelerated computing to improve EV battery cost and range. | Accelerating the Future of Transportation with SES AI’s NVIDIA-Powered Innovation for Electric Vehicles | Technical - Beginner | Explainer |
| Spotlight: Accelerating the Discovery of New Battery Materials with SES AI’s Molecular Universe | false | blog | https://developer.nvidia.com/blog/spotlight-accelerating-the-discovery-of-new-battery-materials-with-ses-ais-molecular-universe | cuML | 2025-05-08T12:00:00.000Z | Survey how GPU-accelerated ML speeds discovery of new battery materials via SES AI's Molecular Universe. | Spotlight: Accelerating the Discovery of New Battery Materials with SES AI’s Molecular Universe | Technical - Beginner | Overview |
| Train with Terabyte-Scale Datasets on a Single NVIDIA Grace Hopper Superchip Using XGBoost 3.0 | false | blog | https://developer.nvidia.com/blog/train-with-terabyte-scale-datasets-on-a-single-nvidia-grace-hopper-superchip-using-xgboost-3-0 | cuML | 2025-08-07T15:25:36.000Z | Analyze how XGBoost 3.0 trains terabyte-scale gradient-boosted trees on a single Grace Hopper Superchip. | Train with Terabyte-Scale Datasets on a Single NVIDIA Grace Hopper Superchip Using XGBoost 3.0 | Technical - Advanced | Explainer |
| Supercharging Fraud Detection in Financial Services with Graph Neural Networks (Updated) | false | blog | https://developer.nvidia.com/blog/supercharging-fraud-detection-in-financial-services-with-graph-neural-networks | cuGraph, cuML, Morpheus | 2025-06-03T03:00:00.000Z | Examine how graph neural networks supercharge fraud detection pipelines in financial services. | Supercharging Fraud Detection in Financial Services with Graph Neural Networks (Updated) | Technical - Intermediate | Explainer |
| Using NetworkX, Jaccard Similarity, and cuGraph to Predict Your Next Favorite Movie | false | blog | https://developer.nvidia.com/blog/using-networkx-jaccard-similarity-and-cugraph-to-predict-your-next-favorite-movie | cuGraph | 2025-02-13T14:00:00.000Z | Learn how to predict movie recommendations using NetworkX Jaccard similarity accelerated by cuGraph. | Using NetworkX, Jaccard Similarity, and cuGraph to Predict Your Next Favorite Movie | Technical - Intermediate | Explainer |
| Supercharge Tree-Based Model Inference with Forest Inference Library in NVIDIA cuML | false | blog | https://developer.nvidia.com/blog/supercharge-tree-based-model-inference-with-forest-inference-library-in-nvidia-cuml | cuML | 2025-06-05T12:00:00.000Z | Learn how cuML Forest Inference Library accelerates tree-ensemble model predictions on GPUs. | Supercharge Tree-Based Model Inference with Forest Inference Library in NVIDIA cuML | Technical - Intermediate | Explainer |
| Stacking Generalization with HPO: Maximize Accuracy in 15 Minutes with NVIDIA cuML | false | blog | https://developer.nvidia.com/blog/stacking-generalization-with-hpo-maximize-accuracy-in-15-minutes-with-nvidia-cuml | cuML | 2025-05-01T15:35:18.000Z | Learn how to maximize model accuracy using stacking generalization and HPO with cuML. | Stacking Generalization with HPO: Maximize Accuracy in 15 Minutes with NVIDIA cuML | Technical - Intermediate | Explainer |
| Kaggle Grandmasters Unveil Winning Strategies for Data Science Superpowers | false | blog | https://developer.nvidia.com/blog/kaggle-grandmasters-unveil-winning-strategies-for-data-science-superpowers | cuDF, cuML | 2025-04-29T14:22:59.000Z | Learn winning data science strategies and GPU acceleration tactics from NVIDIA Kaggle Grandmasters. | Kaggle Grandmasters Unveil Winning Strategies for Data Science Superpowers | Technical - Intermediate | Explainer |
| Building an Interactive AI Agent for Lightning-Fast Machine Learning Tasks | false | blog | https://developer.nvidia.com/blog/building-an-interactive-ai-agent-for-lightning-fast-machine-learning-tasks | cuML | 2025-11-07T14:44:52.000Z | Build an interactive AI agent that automates data cleaning and speeds up machine learning tasks. | Building an Interactive AI Agent for Lightning-Fast Machine Learning Tasks | Technical - Intermediate | Explainer |
| Training XGBoost Models with GPU-Accelerated Polars DataFrames | false | blog | https://developer.nvidia.com/blog/training-xgboost-models-with-gpu-accelerated-polars-dataframes | cuDF | 2025-11-10T16:30:00.000Z | Learn how to train XGBoost models using GPU-accelerated Polars DataFrames through cuDF interoperability. | Training XGBoost Models with GPU-Accelerated Polars DataFrames | Technical - Intermediate | Explainer |
| Grandmaster Pro Tip: Winning First Place in Kaggle Competition with Feature Engineering Using cuDF pandas | false | blog | https://developer.nvidia.com/blog/grandmaster-pro-tip-winning-first-place-in-kaggle-competition-with-feature-engineering-using-nvidia-cudf-pandas | cuDF, cuML | 2025-04-17T20:03:20.000Z | Learn winning feature engineering techniques for tabular Kaggle competitions using cuDF pandas. | Grandmaster Pro Tip: Winning First Place in Kaggle Competition with Feature Engineering Using cuDF pandas | Technical - Intermediate | Explainer |
| Grandmaster Pro Tip: Winning First Place in a Kaggle Competition with Stacking Using cuML | false | blog | https://developer.nvidia.com/blog/grandmaster-pro-tip-winning-first-place-in-a-kaggle-competition-with-stacking-using-cuml | cuML | 2025-05-22T13:30:00.000Z | Learn how stacking ensembles with cuML won a 2025 Kaggle Playground competition. | Grandmaster Pro Tip: Winning First Place in a Kaggle Competition with Stacking Using cuML | Technical - Intermediate | Explainer |
| Simplify Setup and Boost Data Science in the Cloud using NVIDIA CUDA-X and Coiled | false | blog | https://developer.nvidia.com/blog/simplify-setup-and-boost-data-science-in-the-cloud-using-nvidia-cuda-x-and-coiled | cuDF | 2025-05-15T15:31:36.000Z | Set up cloud-based GPU data science workflows using NVIDIA CUDA-X and Coiled. | Simplify Setup and Boost Data Science in the Cloud using NVIDIA CUDA-X and Coiled | Technical - Beginner | Explainer |
| How to Accelerate Community Detection in Python Using GPU-Powered Leiden | false | blog | https://developer.nvidia.com/blog/how-to-accelerate-community-detection-in-python-using-gpu-powered-leiden | cuGraph | 2025-09-23T13:30:00.000Z | Accelerate community detection in Python using GPU-powered Leiden with NVIDIA cuGraph. | How to Accelerate Community Detection in Python Using GPU-Powered Leiden | Technical - Intermediate | How-to |
| How to Spot (and Fix) 5 Common Performance Bottlenecks in pandas Workflows | false | blog | https://developer.nvidia.com/blog/how-to-spot-and-fix-5-common-performance-bottlenecks-in-pandas-workflows | cuDF | 2025-08-22T16:54:44.000Z | Optimize pandas workflows by spotting and fixing five common performance bottlenecks. | How to Spot (and Fix) 5 Common Performance Bottlenecks in pandas Workflows | Technical - Intermediate | How-to |
| NVIDIA cuML Brings Zero Code Change Acceleration to scikit-learn | false | blog | https://developer.nvidia.com/blog/nvidia-cuml-brings-zero-code-change-acceleration-to-scikit-learn | cuML | 2025-03-18T14:42:25.000Z | Learn how cuML delivers zero-code-change GPU acceleration for scikit-learn workflows. | NVIDIA cuML Brings Zero Code Change Acceleration to scikit-learn | Technical - Beginner | Explainer |
| How to Work with Data Exceeding VRAM in the Polars GPU Engine | false | blog | https://developer.nvidia.com/blog/how-to-work-with-data-exceeding-vram-in-the-polars-gpu-engine | cuDF | 2025-06-27T14:00:00.000Z | Configure the Polars GPU engine to process datasets larger than available VRAM efficiently. | How to Work with Data Exceeding VRAM in the Polars GPU Engine | Technical - Intermediate | How-to |
| Reducing CUDA Binary Size to Distribute cuML on PyPI | false | blog | https://developer.nvidia.com/blog/reducing-cuda-binary-size-to-distribute-cuml-on-pypi | CUDA Toolkit, cuML, cuVS | 2025-12-15T14:30:00.000Z | Examine how NVIDIA shrunk CUDA binaries to ship pip-installable cuML wheels via PyPI. | Reducing CUDA Binary Size to Distribute cuML on PyPI | Technical - Advanced | Explainer |
| Mastering the cudf.pandas Profiler for GPU Acceleration | false | blog | https://developer.nvidia.com/blog/mastering-the-cudf-pandas-profiler-for-gpu-acceleration | cuDF | 2025-01-30T14:00:00.000Z | Learn how to use the cudf.pandas profiler to identify and fix GPU acceleration bottlenecks. | Mastering the cudf.pandas Profiler for GPU Acceleration | Technical - Intermediate | Explainer |
| JSON Lines Reading with pandas 100x Faster Using NVIDIA cuDF | false | blog | https://developer.nvidia.com/blog/json-lines-reading-with-pandas-100x-faster-using-nvidia-cudf | cuDF | 2025-02-20T14:00:00.000Z | Accelerate JSON Lines parsing in pandas by 100x using NVIDIA cuDF. | JSON Lines Reading with pandas 100x Faster Using NVIDIA cuDF | Technical - Intermediate | Explainer |
| Building Nemotron-CC, A High-Quality Trillion Token Dataset for LLM Pretraining from Common Crawl Using NVIDIA NeMo Curator | false | blog | https://developer.nvidia.com/blog/building-nemotron-cc-a-high-quality-trillion-token-dataset-for-llm-pretraining-from-common-crawl-using-nvidia-nemo-curator | cuDF, cuML, NeMo Curator | 2025-05-07T13:22:31.000Z | Examine how NeMo Curator builds the Nemotron-CC trillion-token LLM pretraining dataset from Common Crawl. | Building Nemotron-CC, A High-Quality Trillion Token Dataset for LLM Pretraining from Common Crawl Using NVIDIA NeMo Curator | Technical - Intermediate | Explainer |
| Feature Engineering at Scale: Optimizing ML Models in Semiconductor Manufacturing with NVIDIA CUDA‑X Data Science | false | blog | https://developer.nvidia.com/blog/feature-engineering-at-scale-optimizing-ml-models-in-semiconductor-manufacturing-with-nvidia-cuda%E2%80%91x-data-science | cuDF, cuML | 2025-07-17T13:04:06.000Z | Learn how CUDA-X Data Science scales feature engineering for semiconductor manufacturing ML models. | Feature Engineering at Scale: Optimizing ML Models in Semiconductor Manufacturing with NVIDIA CUDA‑X Data Science | Technical - Intermediate | Explainer |
| AI in Manufacturing and Operations at NVIDIA: Accelerating ML Models with NVIDIA CUDA-X Data Science | false | blog | https://developer.nvidia.com/blog/ai-in-manufacturing-and-operations-at-nvidia-accelerating-ml-models-with-nvidia-cuda-x-data-science | CUDA Toolkit, cuDF, cuML | 2025-06-18T12:00:00.000Z | Learn how CUDA-X Data Science accelerates ML across NVIDIA chip manufacturing and operations. | AI in Manufacturing and Operations at NVIDIA: Accelerating ML Models with NVIDIA CUDA-X Data Science | Technical - Intermediate | Explainer |
| Driving Toward Billion-Cell Analysis and Biological Breakthroughs with RAPIDS-singlecell | false | blog | https://developer.nvidia.com/blog/driving-toward-billion-cell-analysis-and-biological-breakthroughs-with-rapids-singlecell | cuML | 2025-06-12T10:00:00.000Z | Explore how RAPIDS-singlecell scales single-cell analysis to billion-cell datasets for biological research. | Driving Toward Billion-Cell Analysis and Biological Breakthroughs with RAPIDS-singlecell | Technical - Intermediate | Explainer |
| Accelerating Real-Time Financial Decisions with Quantitative Portfolio Optimization | false | blog | https://developer.nvidia.com/blog/accelerating-real-time-financial-decisions-with-quantitative-portfolio-optimization | cuML | 2025-12-02T15:51:00.000Z | Analyze how GPU-accelerated cuML enables real-time quantitative portfolio optimization at scale. | Accelerating Real-Time Financial Decisions with Quantitative Portfolio Optimization | Technical - Intermediate | Explainer |
| Enhance Your Training Data with New NVIDIA NeMo Curator Classifier Models | false | blog | https://developer.nvidia.com/blog/enhance-your-training-data-with-new-nvidia-nemo-curator-classifier-models | cuDF, NeMo, NeMo Curator | 2024-12-19T20:08:08.000Z | Enhance training data quality using NeMo Curator's new classifier models for generative AI pipelines. | Enhance Your Training Data with New NVIDIA NeMo Curator Classifier Models | Technical - Intermediate | Explainer |
| Get Started with GPU Acceleration for Data Science | false | blog | https://developer.nvidia.com/blog/get-started-with-gpu-acceleration-for-data-science | cuDF | 2025-02-06T20:07:48.000Z | Practice GPU-accelerated data science workflows for faster processing of large, complex datasets. | Get Started with GPU Acceleration for Data Science | Technical - Beginner | Tutorial |
| Accelerating Time Series Forecasting with RAPIDS cuML | false | blog | https://developer.nvidia.com/blog/accelerating-time-series-forecasting-with-rapids-cuml | cuML | 2025-01-16T14:20:10.000Z | Learn how to accelerate time-series forecasting using RAPIDS cuML on GPUs. | Accelerating Time Series Forecasting with RAPIDS cuML | Technical - Beginner | Explainer |
| Efficient Transforms in cuDF Using JIT Compilation | false | blog | https://developer.nvidia.com/blog/efficient-transforms-in-cudf-using-jit-compilation | cuDF | 2025-08-07T18:06:42.000Z | Learn how cuDF uses JIT compilation to deliver efficient GPU-accelerated ETL transforms. | Efficient Transforms in cuDF Using JIT Compilation | Technical - Advanced | Explainer |
| Efficiently Scaling Polars GPU Parquet Reader | false | blog | https://developer.nvidia.com/blog/efficiently-scaling-polars-gpu-parquet-reader | cuDF | 2025-04-10T13:30:00.000Z | Examine engineering work scaling the Polars GPU Parquet reader for large datasets. | Efficiently Scaling Polars GPU Parquet Reader | Technical - Advanced | Explainer |
| The Kaggle Grandmasters Playbook: 7 Battle-Tested Modeling Techniques for Tabular Data | true | blog | https://developer.nvidia.com/blog/the-kaggle-grandmasters-playbook-7-battle-tested-modeling-techniques-for-tabular-data | cuDF, cuML | 2025-09-18T14:29:36.000Z | Learn seven battle-tested modeling techniques for tabular data from Kaggle Grandmasters. | The Kaggle Grandmasters Playbook: 7 Battle-Tested Modeling Techniques for Tabular Data | Technical - Intermediate | Explainer |
| 3 pandas Workflows That Slowed to a Crawl on Large Datasets—Until We Turned on GPUs | true | blog | https://developer.nvidia.com/blog/3-pandas-workflows-that-slowed-to-a-crawl-on-large-datasets-until-we-turned-on-gpus | cuDF | 2025-07-18T10:00:00.000Z | Accelerate three common pandas workflows on large datasets by enabling GPU execution. | 3 pandas Workflows That Slowed to a Crawl on Large Datasets—Until We Turned on GPUs | Technical - Beginner | Explainer |
| How NVIDIA DGX Spark’s Performance Enables Intensive AI Tasks | false | blog | https://developer.nvidia.com/blog/how-nvidia-dgx-sparks-performance-enables-intensive-ai-tasks | cuDF, cuML | 2025-10-24T13:00:00.000Z | Examine how DGX Spark's memory and compute enable intensive local AI developer workloads. | How NVIDIA DGX Spark’s Performance Enables Intensive AI Tasks | Technical - Intermediate | Explainer |
| Even Faster and More Scalable UMAP on the GPU with RAPIDS cuML | false | blog | https://developer.nvidia.com/blog/even-faster-and-more-scalable-umap-on-the-gpu-with-rapids-cuml | cuML | 2024-10-31T17:24:07.000Z | Examine scalable GPU UMAP in RAPIDS cuML for bioinformatics, NLP, and ML preprocessing. | Even Faster and More Scalable UMAP on the GPU with RAPIDS cuML | Technical - Advanced | Explainer |
| How to GPU-Accelerate Model Training with CUDA-X Data Science | true | blog | https://developer.nvidia.com/blog/how-to-gpu-accelerate-model-training-with-cuda-x-data-science | cuML | 2025-09-25T13:30:00.000Z | Accelerate ML model training on GPUs using CUDA-X Data Science libraries like cuML and cuDF. | How to GPU-Accelerate Model Training with CUDA-X Data Science | Technical - Beginner | How-to |
| Try GPU Polars Notebook | true | hands-on | https://colab.research.google.com/github/rapidsai-community/showcase/blob/main/accelerated_data_processing_examples/polars_gpu_engine_demo.ipynb | cuDF | 2024-09-17T13:11:07.000Z | Build accelerated DataFrame workflows in Colab using the Polars GPU engine. | Try GPU Polars Notebook | Technical - Beginner | Tutorial |
| Try cuGraph Notebook | true | hands-on | https://colab.research.google.com/github/rapidsai-community/showcase/blob/main/getting_started_tutorials/accelerated_networkx_demo.ipynb | cuGraph | 2026-02-12T21:47:07.000Z | Practice accelerating NetworkX graph analytics with nx-cugraph in a hosted Colab notebook. | Try cuGraph Notebook | Technical - Beginner | Tutorial |
| Try cuDF pandas Notebook | true | hands-on | https://colab.research.google.com/github/rapidsai-community/showcase/blob/main/getting_started_tutorials/cudf_pandas_colab_demo.ipynb | cuDF | 2024-10-25T16:35:06.000Z | Practice GPU-accelerating pandas workflows with cuDF pandas in a hosted Colab notebook. | Try cuDF pandas Notebook | Technical - Beginner | Tutorial |
| Try cuML Notebook | true | hands-on | https://colab.research.google.com/github/rapidsai-community/showcase/blob/main/getting_started_tutorials/cuml_sklearn_colab_demo.ipynb | cuML | 2025-03-19T18:33:24.000Z | Practice running cuML's zero-code-change scikit-learn accelerator in a hosted Colab notebook. | Try cuML Notebook | Technical - Beginner | Tutorial |
| Try XGBoost Notebook | true | hands-on | https://colab.research.google.com/drive/19sS-jsREr_suJvOavLFpRsxpICUvnQ7X | cuML | 2025-01-01T05:00:00.000Z | Practice GPU-accelerated data science techniques in this interactive Google Colab notebook. | Try XGBoost Notebook | Technical - Beginner | Tutorial |
| 7 Drop-In Replacements to Instantly Speed Up Your Python Data Science Workflows | true | blog | https://developer.nvidia.com/blog/7-drop-in-replacements-to-instantly-speed-up-your-python-data-science-workflows | cuDF, cuGraph | 2025-08-01T19:45:50.000Z | Learn seven drop-in library replacements to instantly speed up Python data science workflows. | 7 Drop-In Replacements to Instantly Speed Up Your Python Data Science Workflows | Technical - Beginner | Explainer |

[Download the raw results data (JSON)](https://developer.nvidia.com/search-data/data_science.json)



* * *

## The Accelerated Data Science Ecosystem

Data practitioners in open source libraries, commercial software, and industries are driving innovation with CUDA-X.

Open-Source Libraries

Platforms

Industry Adoption

We're committed to simplifying, unifying, and accelerating data science for the open-source community.

[![Data Science Open-Source Library - CuPy](https://developer.download.nvidia.com/images/logos/cupy-logo.svg)](https://docs.cupy.dev/en/stable/install.html)

[![ Data Science Open-Source Library - Dask](https://developer.download.nvidia.com/images/logos/dask-logo.svg)](https://docs.dask.org/en/stable/gpu.html)

[![Data Science Open-Source Library - Dmlc XGBoost](https://developer.download.nvidia.com/images/logos/dmlc-xgboost-logo.svg)](https://xgboost.readthedocs.io/en/stable/gpu/)

[![Data Science Open-Source Library - NetworkX](https://developer.download.nvidia.com/images/logos/networkx-logo.svg)](https://networkx.org/documentation/stable/backends.html)

[![ Data Science Open-Source Library - Polars](https://developer.download.nvidia.com/images/logos/polars-logo.svg)](https://docs.pola.rs/user-guide/gpu-support/)

[![Data Science Open-Source Library - PyG](https://developer.download.nvidia.com/images/logos/pyg-logo.svg)](https://docs.rapids.ai/api/cugraph/stable/graph_support/pyg_support/)

[![Data Science Open-Source Library - Scikit Learn](https://developer.download.nvidia.com/images/logos/scikit-learn-logo.svg)](https://docs.rapids.ai/api/cuml/stable/)

[![ Data Science Open-Source Library - scverse](https://developer.download.nvidia.com/images/logos/scverse-logo.svg)](https://github.com/scverse/rapids-singlecell)

Use CUDA-X libraries in the most popular data science and machine learning platforms.

[![Data Science Platform - Amazon SageMaker](https://developer.download.nvidia.com/images/logos/amazon-sagemaker-logo.svg)](https://docs.rapids.ai/deployment/stable/cloud/#amazon-web-services)

[![Data Science Platform - Anaconda](https://developer.download.nvidia.com/images/logos/anaconda-logo.svg)](https://anaconda.org/rapidsai/rapids)

[![Data Science Platform - Azure Machine Learning](https://developer.download.nvidia.com/images/logos/azure-machine-learning-logo.svg)](https://docs.rapids.ai/deployment/stable/cloud/#microsoft-azure)

[![Data Science Platform - Coiled](https://developer.download.nvidia.com/images/logos/logo-coiled.svg)](https://docs.coiled.io/user_guide/clusters/gpu-rapids.html)

[![Data Science Platform - Databricks](https://developer.download.nvidia.com/images/logos/databricks-logo.svg)](https://docs.rapids.ai/deployment/stable/platforms/databricks/)

[![Data Science Platform - Google Colab](https://developer.download.nvidia.com/images/logos/google-colab-logo.svg)](https://docs.rapids.ai/deployment/stable/platforms/colab/)

[![Data Science Platform - Kaggle](https://developer.download.nvidia.com/images/logos/logo-kaggle.svg)](https://www.kaggle.com/datasets/cdeotte/rapids)

[![Data Science Platform - Snowflake](https://developer.download.nvidia.com/images/logos/snowflake-logo.svg)](https://www.snowflake.com/en/blog/container-runtime-GPU-training-inference/)

Industry leaders are driving innovation with CUDA-X.

 ![Data Science Industry Adoption - bunq](https://developer.download.nvidia.com/images/logos/bunq-logo.svg)

bunq improved fraud detection accuracy by accelerating model training 100x and data processing 5x using NVIDIA cuDF and cuML libraries.

[Read Blog](https://blogs.nvidia.com/blog/europe-financial-services-ai/)

 ![Data Science Industry Adoption - CapitalOne](https://developer.download.nvidia.com/images/logos/capital-one-logo.svg)

Capital One accelerated its financial and credit analysis pipelines with NVIDIA cuDF and cuML, improving model training by 100x.

[Watch On-Demand Session](https://www.nvidia.com/en-us/on-demand/session/gtcsj20-s22136/)

 ![Data Science Industry Adoption - Checkout.com](https://developer.download.nvidia.com/images/logos/checkout-logo.svg)

Checkout.com accelerated their data analysis workflows from minutes to seconds with NVIDIA cuDF.

[Read Blog](https://blogs.nvidia.com/blog/europe-financial-services-ai/)

 ![Data Science Industry Adoption - Linkedin](https://developer.download.nvidia.com/images/logos/linkedin-logo.svg)

LinkedIn developed DARWIN to enable faster data analysis on NVIDIA cuDF.

[Watch On-Demand Session](https://www.nvidia.com/en-us/on-demand/session/gtcspring23-s51399/)

 ![Data Science Industry Adoption - Tgen](https://developer.download.nvidia.com/images/logos/tgen-logo.svg)

TGen cut analysis time on 4-million-cell datasets from 10 hours to three minutes with RAPIDS-singlecell, built on NVIDIA cuML.

[Read Customer Story](https://www.nvidia.com/en-us/customer-stories/reduce-single-cell-spatial-analysis-from-hours-to-minutes/)

* * *

## Join the Community

 ![](https://developer.download.nvidia.com/icons/m48-people-group.svg)
### Join the Accelerated Data Science Community on Slack

 ![](https://developer.download.nvidia.com/icons/m48-email-settings.svg)
### Sign Up for the Data Science Newsletter

* * *

## Ethical AI 

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their supporting team to ensure their application meets requirements for the relevant industry and use case and addresses unforeseen product misuse.   
  
Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).

### Download CUDA-X Libraries for Data Science today.

[Download](https://docs.rapids.ai/install/?_gl=1*kwbd1w*_ga*MTE4NDAwMTQ1NS4xNzA5NzcwODcw*_ga_RKXFW6CM42*czE3NTIxODk0OTQkbzk1JGcwJHQxNzUyMTg5NDk0JGo2MCRsMCRoMA)