HPE Ezmeral Container Platform is a software platform for deploying and managing containerized enterprise applications with 100% open-source Kubernetes at scale—for use cases including machine learning, analytics, IoT/edge, CI/CD, and application modernization.
Kubernetes has emerged as the de-facto open-source standard for container orchestration and a fundamental building block for cloud-native architectures. However, while it is straightforward to deploy modern, cloud-native applications in containers, these represent a small portion of enterprise applications. The vast majority of enterprise applications are still non-cloud-native or monolithic. The challenge is to deploy and run these monolithic applications in containers, without re-architecting them.
In addition, as enterprise organizations extend the use of containers and Kubernetes beyond development and testing to production environments, they need to address key considerations including security and data persistence.
HPE Ezmeral Runtime Enterprise is an enterprise-grade container orchestration platform that is designed for the containerization of both cloud-native and non-cloud-native monolithic applications with persistent data. It deploys 100% open-source Kubernetes for orchestration, provides a state-of-the-art file system and data fabric for persistent container storage, and provides enterprises with the ability to deploy non-cloud-native AI and Analytics workloads in containers. Enterprises can now easily extend the agility and efficiency benefits of containers to more of their enterprise applications—running on either bare-metal or virtualized infrastructure, on-premises, in multiple clouds, or at the edge.
HPE Ezmeral Runtime Enterprise includes open-source Kubernetes for orchestration, a proven system for deploying non-cloud-native AI and Analytics applications in containers, as well as a state-of-the-art file system and data fabric for persistent container storage.
Are your deployments of distributed AI and analytics applications complex and time-consuming?
Does it take weeks or even months for large-scale enterprise implementation on infrastructure that you aren’t certain has the capabilities to support your objectives? Deploying HPE Apollo Systems for BlueData EPIC Software, based on the HPE Elastic Platform for Analytics (EPA) architecture, provides an efficient, flexible and cost effective solution addressing the evolving requirements of these workloads. This cloud-ready infrastructure can be seamlessly extended in a contiguous hybrid cloud with Amazon Web Services, Google Cloud Platform, and Microsoft Azure. The logical and physical separation of compute and storage enables you to increase efficiency and flexibility. HPE Apollo Systems for BlueData Software helps eliminate the need for a dedicated DevOps team by provisioning and configuring components, facilitating cloud deployments, and jump-starting your AI and analytics initiatives..
Much like pre-DevOps software development, data science organizations still spend a significant amount of time and effort when moving projects from development to production. Model version control and code sharing is manual, and there is a lack of standardization on tools and frameworks, making it tedious and time-consuming to productize machine learning models.
HPE Ezmeral Machine Learning Ops (HPE Ezmeral ML Ops) extends the capabilities of the HPE Ezmeral Runtime Enterprise and brings DevOps-like agility to enterprise machine learning. With the HPE Ezmeral ML Ops, enterprises can implement DevOps processes to standardize their ML workflows.
HPE Ezmeral ML Ops provides data science teams with a platform for their end-to-end data science needs with the flexibility to run their machine learning or deep learning (DL) workloads on-premises, in multiple public clouds, or a hybrid model and respond to dynamic business requirements in a variety of use cases.
Does your enterprise need a persistent data store that unifies data and modernizes applications?
HPE Ezmeral Data Fabric ingests and stores different data types across data lakes, on-premises, cloud, and edge environments to provide a persistent data store that can be leveraged across multiple use cases. The global data fabric and namespace enables teams to access data wherever it resides while maintaining governance, security, and geographic data regulations. Broad API support allows legacy and modern apps and AI/ML tools to access common datasets without changing existing access patterns or moving data before analysis can begin. Customized for analytics, customers can run Apache™ Spark, Delta Lake, or Livy on top of this foundational layer to achieve enterprise-wide impact. This solution is an excellent starting point for enterprises with a board-level mandate to use data to create a digital advantage for the organization.
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