A ‘Herculean’ HPE Ezmeral Software AI Makeover: 7 Things You Need To Know
HPE released a new version of its Ezmeral Software data fabric that transforms the SaaS offering into a much more powerful platform for AI artificial intelligence and ML based workloads.
Consumption Billing For AI Workloads And Unified Analytics
HPE Ezmeral Software has a new consumption based subscription model that is “extremely, extremely price-competitive with the public clouds,” said Shattuck.
The new simplified Ezmeral platform is aimed at providing “predictable and transparent economics” that effectively eliminate the “unpredictable costs and risk of vendor lock” of public cloud environments, said HPE.
The new release of HPE Ezmeral Software effectively reduces the number of software products or tools it takes to produce AI and machine learning based applications in a public cloud or multi-cloud environment, said Shattuck.
In fact, Shattuck said, the new release of Ezmeral has effectively reduced by one third to one half the number of tools a customer would have to subscribe to from a public cloud provider in order to develop and deploy AI based applications.
HPE Ezmeral’s multi-cloud capabities means the number of tools it takes to run and deploy multi-cloud AI based applications is reduced even further, said Shattuck. That’s because there will be a “commonality” of tools with the Ezmeral edge to cloud data fabric across on-premise and multiple cloud environments, he said.
“We see the number of tools that people are going to have to adopt dramatically reduced and with that the corresponding associated price cost,” he said.
HPE Ezmeral at the data fabric level provides file, object, real-time streaming, and database access at a single monthly subscription price, said Shattuck. “We think it is going to be significantly less expensive than if you had bought all of those foundational data services from one of the (public) cloud providers,” he said.
If that isn’t enough, HPE is also providing within its unified analytics software offering “all the tool sets for data engineering, data analysts, some visualization, and machine learning operationalization,” said Shattuck. “That is again another four or five tools you would have to acquire within one of the ([public cloud) providers.”