Gathering business insights can be a pain, especially when you��re dealing with countless data points. It��s no secret that GPUs can be a time-saver for data scientists. Rather than wait for a single query to run, GPUs help speed up the process and get you the insights you need quickly. In this video, Allan Enemark, RAPIDS data visualization lead, uses a US Census dataset with over 300��
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Visualization brings data to life, unveiling hidden patterns and insights through accessible visuals, and empowering you and your organization to perceive the invisible, make informed decisions, and fully leverage your data. Especially when working with large datasets, interaction can be difficult as render and compute times become prohibitive. Switching to RAPIDS libraries, such as cuDF��
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If you are looking to take your machine learning (ML) projects to new levels of speed and scalability, GPU-accelerated data analytics can help you deliver insights quickly with breakthrough performance. From faster computation to efficient model training, GPUs bring many benefits to everyday ML tasks. Update: The below blog describes how to use GPU-only RAPIDS cuDF��
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This post is part of a series on accelerated data analytics. Digital advancements in climate modeling, healthcare, finance, and retail are generating unprecedented volumes and types of data. IDC says that by 2025, there will be 180 ZB of data compared to 64 ZB in 2020, scaling up the need for data analytics to turn all that data into insights. NVIDIA provides the RAPIDS suite of��
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This post is part of a series on accelerated data analytics. Update: The below blog describes how to use GPU-only RAPIDS cuDF, which requires code changes. RAPIDS cuDF now has a CPU/GPU interoperability (cudf.pandas) that speeds up pandas code by up to 150x with zero code changes. At GTC 2024, NVIDIA announced that the cudf.pandas library is now GA. At Google I/O��
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