With the rapid growth of generative AI, CIOs and IT leaders are looking for ways to reclaim data center resources to accommodate new AI use cases that promise greater return on investment without impacting current operations. This is leading IT decision makers to reassess past infrastructure decisions and explore strategies to consolidate traditional workloads into fewer…
]]>Spark MLlib is a key component of Apache Spark for large-scale machine learning and provides built-in implementations of many popular machine learning algorithms. These implementations were created a decade ago, but do not leverage modern computing accelerators, such as NVIDIA GPUs. To address this gap, we have recently open-sourced Spark RAPIDS ML (NVIDIA/spark-rapids-ml)…
]]>According to IDC, the volume of data generated each year is growing exponentially. IDC’s Global DataSphere projects that the world will generate 221 ZB of data by 2026. This data holds fantastic information. But as the volume of data grows, so does the processing cost. As a data scientist or engineer, you’ve certainly felt the pain of slow-running, data-processing jobs.
]]>RAPIDS Accelerator for Apache Spark v21.10 is now available! As an open source project, we value our community, their voice, and requests. This release constitutes community requests for operations that are ideally suited for GPU acceleration. Important callouts for this release: RAPIDS Accelerator for Apache Spark is growing at a great pace in both functionality and…
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