Data-intensive computing is a class of parallel computing applications designed to use data parallel approach to process large volumes of data.
A cloud deployment model reflects a sort of cloud ecosystem, primarily characterized by size, ownership, as well as access to cloud computing.
Grid computing is computing systems which come together to assessing data-sets that are massive as a virtual supercomputer.
Edge computing calculating optimizing applications data from a couple of fundamental nodes into the edge of their Internet which makes the connection.
Cloud computing for deep learning calculates the lesser costs of GPU power and allows large data-sets to ingested and managed train algorithms.