Infrastructure for machine learning, AI requirements, examples

IT owes its existence as a professional discipline to companies seeking a competitive edge from information. Today, organizations are awash in data, but the technology to process and analyze it often struggles to keep up with the deluge of every machine, application and sensor emitting an endless stream of telemetry. An explosion in unstructured data has proved to be particularly challenging for traditional information systems based on structured databases, which has sparked the development of new algorithms based on machine learning and deep learning. This, in turn, has led to a need for organizations to either buy or build systems and infrastructure for machine learning, deep learning and AI workloads. That's because the nexus of geometrically expanding unstructured data sets, a surge in machine learning (ML) and deep learning (DL) research, and exponentially more powerful hardware designed to parallelize and accelerate ML and DL workloads have fueled an explosion of interest in enterprise AI applications.