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Large companies across a variety of industries have discovered hidden business value within existing data, and using machine learning algorithms to find hidden insight within that data has quickly become the norm. In spite of all of its benefits, machine learning for data analytics poses some challenges, particularly where storage infrastructure is concerned. Because data can contain hidden value, organizations may be less inclined to purge aging data. This causes storage to be consumed at an accelerated rate, complicating capacity planning efforts. Furthermore, the actual analytical processes generate an additional load on the underlying storage infrastructure. Somewhat ironically, several vendors have begun using AI as a tool for solving problems created by big data analytics. As it stands now, they have not based their machine learning for analytics efforts around one single technology, but rather on a disparate collection of technologies. The Lambda architecture When it comes to using AI for things like workload profiling ...