ML helps computers to acquire a knowledge without extensive programming and its performance enhances in compute and data by improving its development.

High-performance computing and big-data applications leverage virtualization as it helps in concurrent support for different software infrastructures, creating resource pools, consistent research surroundings, multi-domain data security, problem diagnosis and elasticity, effective load balancing, and QoS. High-Performance Computing (HPC) and big data merged together so ML can be consumed as services from different cloud environments. These applications have huge data volumes with data-compliance and security policies to follow. Customers like to opt private cloud for hosting these ML applications with huge data which required more compute resources. 

We can configure MLaaS in a private cloud using vRealize Automation to provide GPU-powered ML services for design/power users. The workflow can help to build an ML-based blueprint that can fulfill the particular requirements of design users.