Optimized Architectures: Designing Storage Architectures for Big Data and AI/ML Workloads

Optimized Architectures Designing Storage Architectures for Big Data and AIML Workloads2 min

Artificial intelligence (AI), machine learning (ML), and other big data applications and systems that generate and use massive amounts of data are rising rapidly. According to recent surveys, well over half of enterprises say they are using ML today, and nearly all will within a few years. Big data and AI/ML workloads necessitate the ability to process and analyze massive volumes of data, both structured and unstructured; meanwhile, with hybrid cloud and multicloud strategies resulting in multiple locations for said data, including on premises, off premises, and edge, enterprises must reconsider numerous issues around data management, current and future storage capacity, and efficiency to define an architectural sweet spot that can manage these data sources and applications.

 

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