Delivering AI/ML without proper Dataops is just wishful thinking!

DataOps Team Process

Given the iterative nature of AI/ML projects, having an agile process of building fast and reliable data pipelines (referred to as DataOps) has been the key differentiator in the ML projects that succeeded (unless there was a very exhaustive feature store available which is typically never the case).

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What‌ ‌is‌ ‌DataOps,‌ ‌and‌ ‌why‌ ‌it’s‌ ‌a‌ ‌top‌ ‌trend‌

Platform Ops for AI

DataOps‌ ‌emerged‌ ‌seven‌ ‌years‌ ‌ago‌ to refer to ‌best‌ ‌practices‌ for ‌getting‌ ‌proper‌ ‌analytics,‌ ‌and research firm Gartner calls it a major trend encompassing several steps in the data lifecycle. Just‌ as‌ ‌the‌ ‌DevOps‌ ‌trend‌ ‌led‌ ‌to‌ ‌a‌ ‌better‌ ‌process‌ ‌for‌ ‌collaboration‌ ‌between‌ ‌‌developers‌ ‌and‌ ‌operations‌ ‌teams,‌ ‌DataOps‌ ‌refers‌ ‌to closer collaboration between various teams handling data and operations teams deploying data into applications.

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University of Pisa leans into the I/O challenge AI applications create

At a time when workloads that employ machine and deep learning algorithms are being built and deployed more frequently, organizations need to optimize I/O throughput in a way that enables those workloads to cost-effectively share the expensive GPU resources used to train AI models. Case in point: the University of Pisa, which has been steadily expanding the number of GPUs it makes accessible to AI researchers in a green datacenter optimized for high-performance computing (HPC) applications.

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