Learn the best practices for performance analytics and maintenance of a deep learning system. As GPU technology continues to advance, the demand for faster data continues to grow. In deep learning, input pipelines are responsible for a complex chain of actions that ultimately feed data into GPU memory, including reading from storage and pre-processing data. These pipelines bring together multiple hardware systems—networking, CPUs, and storage—along with sophisticated software systems to drive the data movement and transformation.
– AI is a data Pipeline
– Don’t throw your data into Data Lake
– Cloud or not to cloud?
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Unstructured data specialist covering Nordic region and based in Sweden at Pure Storage, focused on AI (DL/ML), streaming and modern analytics, life science rapid data restore, etc… Working as a practitioner of data architecting, with designing, deploying and helping organisations to manage their data architecture. +28 years’ experience in IT industry (manufacturing side), last 23 year with Storage (SAN & NAS) and infrastructure performance management. Primary focus is technical presales on All Flash based storage solutions and spokesperson (evangelist).
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