HPE Ezmeral ML Ops standardizes processes and provides pre-packaged tools to build, train, deploy and monitor machine learning workflows, giving you DevOps-like speed and agility at every stage of the ML lifecycle.

Model Build
Pre-packaged, self-service sandbox environments

Quickly spin-up environments with your preferred data science tools to explore a variety of enterprise data sources and simultaneously experiment with multiple machine learning or deep learning frameworks to pick the best fit model for the business problems you need to address.

HPE Ezmeral Early Access

HPE Ezmeral Early Access lets developers try new products before they’re released, getting hands-on experience with new features and capabilities.

Model Training
Single node or distributed multi-node containerized environments

Self-service, on-demand environments for development and test or production workloads. Highly performant training environments—with separation of compute and storage—that securely access shared enterprise data sources in on-premises or cloud-based storage.

Multi-tenancy and data isolation on shared infrastructure and data sources

Leverage multi-tenancy and data isolation to ensure logical separation between each project, group, or department within the organization. The platform integrates with enterprise security and authentication mechanisms such as LDAP, Active Directory, and Kerberos.

Model Deployment and Monitoring
Deploy to containers with complete visibility across the ML pipeline

Deploy the model’s runtime image (Python, R, H2O, etc) to a containerized endpoint. With the model registry, track model versions, and seamlessly update models when needed. Have complete visibility into runtime resource usage. Track, measure, and report model performance, save and inspect inputs and outputs for each scoring request. Integrations with third party software report model accuracy and interpretability.

Hybrid cloud ready

Run the HPE Ezmeral ML Ops software on-premises on any infrastructure, on multiple public clouds (Amazon® Web Services, Google® Cloud Platform, or Microsoft® Azure), or in a hybrid model, providing effective utilization of resources and lower operating costs.

CI/CD. A/B testing and canary testing

HPE Ezmeral ML Ops enables source control with out of the box integration tools such as GitHub. Store multiple models (multiple versions with metadata) for various runtime engines in the model registry. Run A/B testing or Canary testing to validate the model before large-scale deployment. An integrated project repository eases collaboration and provides lineage tracking to improve auditability.

  • 53%
    increased profitabilityForrester%3A%20Operationalize%20Machine%20Learning%2C%20June%202020
  • 52%
    better customer experienceForrester%3A%20Operationalize%20Machine%20Learning%2C%20June%202020
  • 49%
    better adoption of data science best practicesForrester%3A%20Operationalize%20Machine%20Learning%2C%20June%202020



HPE Ezmeral ML Ops overcomes “last mile” challenges with a platform that delivers a cloud-like experience, combined with pre-packaged tools, to operationalize the machine learning lifecycle from pilot to production.

HPE Ezmeral ML Ops

A software solution that extends the capabilities of HPE Ezmeral Runtime Enterprise to support the entire ML lifecycle by implementing DevOps-like processes to standardize and accelerate machine learning workflows, providing data science teams with one-click deployment for distributed AI/ML environments and secure access to the data they need.