HPE Machine Learning Ops

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.

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.

HPE 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.

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.

Run the HPE 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.