HPE Technology Partner Program
Why Partner?
This program leverages the open-source Kubernetes ecosystem and our modern ISV partners that are part of HPE’s Technology Partner Program to provide faster time to value and adopt integrated solutions that combine HPE Ezmeral software with validated industry-leading, third-party, commercial and open-source applications. Become a partner and find out how we can help you do more with specialized validated applications on HPE Ezmeral Runtime Enterprise to meet your workload needs, from AI/ML, Big data and Analytics to Monitoring, Data Protection, Security and Developer tools.
Overview
Data science and analytics ISVs are looking to use containerized environments to support the collaborative processes that enable modern analytics and machine learning projects.
The HPE Ezmeral Ecosystem program is a best-in-class ISV program that validates third party software offers which enhance the HPE Ezmeral value proposition and provide our customers with a rich choice of validated applications via the HPE GreenLake Ecosystem.
Product Integration
Our HPE Ezmeral software portfolio includes:
- HPE Ezmeral Runtime Enterprise: A unified container software platform built on open source Kubernetes and designed for both cloud-native applications and non-cloud-native applications running on any infrastructure either on-premises, in multiple public clouds, in a hybrid model, or at the edge.
- HPE Ezmeral Data Fabric: Integrating different data types, tools, and technologies is costly, time consuming, and error prone. HPE Ezmeral Data Fabric simplifies data management by unifying any data type from a wide variety of technologies into a single database. A single autonomous infrastructure scales as needed without impacting service levels, security, or data resilience.
- HPE Ezmeral ML Ops: Bring DevOps-like speed and agility to ML workflows with support for every stage of the machine learning lifecycle: from sandbox experimentation with your choice of ML/DL frameworks, to model training on containerized distributed clusters, to deploying and tracking models in production.