Storage strategies for machine learning and AI workloads


Businesses are increasingly using data assets to accelerate their competitiveness and drive greater revenue. Part of this strategy is to use machine learning and AI tools and technologies. But AI workloads have significantly different data storage and computing needs than generic workloads. AI and machine learning workloads require huge amounts of data both to build and train the models and to keep them running. When it comes to storage for these workloads, high-performance and long-term data storage are the most important concerns.

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