HPE Discover Virtual Experience wrapped up last week, but I have much to think about and report. The HPE team did an excellent job pulling together a conference where we saw many different living rooms and home offices. Tough job; well done.
The release of a new software portfolio from HPE may sound more to the interest of enterprise architects, but I have already seen demos of where this also aids the coming together of OT and IT in order to bring the production side of an enterprise into more of a value to the enterprise. This is important toward the counteracting of recent enterprise history where production was a “black box” and corporate financial geniuses viewed it as something that could be moved around chasing low cost.
From the blog of Kumar Sreekanti, CTO and head of software at HPE, we learn about the coming together of Ezmeral—brand name for the software portfolio.
Digital transformation is being amplified by an order of magnitude. In fact, many business leaders that I’ve spoken with are now embracing a digital-first strategy—to compete and thrive in the midst of a global pandemic. And the enterprises that use data and artificial intelligence effectively are better equipped to evolve rapidly in this dynamic environment. Now these data-driven transformation initiatives are being accelerated to enable faster time-to-market, increased innovation, and greater responsiveness to the business and their customers.
As CTO and head of software at HPE, my focus is on delivering against our edge-to-cloud strategy and vision of providing everything as a service. Software is a very critical and important component of this strategy. It’s also essential to helping our customers succeed in their data-driven digital transformation journeys, now more than ever.
We’re committed to providing a differentiated portfolio of enterprise software to help modernize your applications, unlock insights from your data, and automate your operations—from edge to cloud. Today, we announced that we’ve unified our software portfolio with a new brand: HPE Ezmeral.
The HPE Ezmeral portfolio allows you to:
- Run containers and Kubernetes at scale to modernize apps, from edge to cloud
- Manage your apps, data, and ops – leveraging AI and analytics for faster time-to-insights
- Ensure control for governance, compliance, and lower costs
- Provide enterprise-grade security and authentication to reduce risk
Business innovation relies on applications and data. The apps and data running the enterprise now live everywhere—in data centers, in colocation centers, at the edge, and in the cloud. Most of the applications running businesses today are primarily non-cloud-native; and data is everywhere, with more and more data being generated at edge. Our customers are having real issues with non-cloud-native systems that will not or cannot move to the public cloud due to data gravity, latency, application dependency, and regulatory compliance reasons. Data has gravity, so our customers want to bring compute to the data not data to the compute. And because data is exploding, it’s driving the need for AI and machine learning at enterprise-scale—with the ability to harness and leverage petabytes of data.
Our customers want flexibility and openness; they want to eliminate lock-in. They want pay-per-use consumption in an as-a-service model. They want open solutions that give them the best of both worlds—with a modern cloud experience in any location, from edge to cloud. We address these needs by providing HPE GreenLake in the environment of your choice, with a consistent operating model, and with visibility and governance across all enterprise applications and data. Our software provides differentiated IP to deliver these cloud services through HPE GreenLake.
And in today’s news, we announced new cloud services from HPE GreenLake. This includes new HPE GreenLake cloud services for containers and machine learning operations—powered by our HPE Ezmeral Container Platform software to run containerized applications with open source Kubernetes, and HPE Ezmeral ML Ops software to operationalize the machine learning model lifecycle.
