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 As data scientists shift from using traditional analytics to leveraging AI applications that better model complex market demands, traditional CPU-based processing can no longer keep up without compromising either speed or cost. The growing adoption of AI in analytics has created the need for a new framework to process data quickly and cost efficiently with GPUs.
 
-The RAPIDS Accelerator for Apache Spark combines the power of the <a href="github.com/rapidsai/cudf/">RAPIDS cuDF</a> library and the scale of the Spark distributed computing framework.  The RAPIDS Accelerator library also has a built-in accelerated shuffle based on <a href="https://github.com/openucx/ucx/">UCX</a> that can be configured to leverage GPU-to-GPU communication and RDMA capabilities. 
+The RAPIDS Accelerator for Apache Spark combines the power of the <a href="https://github.com/rapidsai/cudf/">RAPIDS cuDF</a> library and the scale of the Spark distributed computing framework.  The RAPIDS Accelerator library also has a built-in accelerated shuffle based on <a href="https://github.com/openucx/ucx/">UCX</a> that can be configured to leverage GPU-to-GPU communication and RDMA capabilities.