HPE Ezmeral ML Ops

Describes the HPE Ezmeral ML Ops solution and how it relates to HPE Ezmeral Runtime Enterprise.

HPE Ezmeral ML Ops is an end-to-end data science machine learning (ML) solution with the flexibility to run on-premises, in multiple public clouds, or in a hybrid model and respond to dynamic business requirements in a variety of use cases.

The HPE Ezmeral ML Ops solution supports every stage of the machine learning (ML) lifecycle—from data preparation to model build, model training, model deployment, collaboration, and monitoring.

Key Features

Key features of HPE Ezmeral ML Ops include the following:

Model building Pre-packaged, self-service sandbox environments: Sandbox environments with any preferred data science tools—such as TensorFlow, Apache Spark, Keras, PyTorch and more—to enable simultaneous experimentation with multiple ML or deep learning (DL) frameworks.
Model training Scalable training environments with secure access to Big Data: On-demand access to scalable environments—single node or distributed multi-node clusters—for development and test or production workloads. Patented innovations provide highly performant training environments—with compute and storage separation—that can securely access shared enterprise data sources on-premises or in cloud-based storage.
Model deployment Flexible, scalable, endpoint deployment: HPE Ezmeral ML Ops deploys the model’s native runtime image, such as Python, R, H2O, into a secure, highly available, load-balanced, and containerized HTTP endpoint. An integrated model registry enables version tracking and seamless updates to models in production. Autoscaling from HPE Ezmeral ML Ops dynamically scales nodes for scoring engines.
Model monitoring End-to-end visibility across the ML lifecycle: Complete visibility into runtime resource usage such as GPU, CPU, and memory utilization. Ability to track, measure, and report model performance along with third-party integrations track accuracy and interpretability.
Collaboration Enable CI/CD workflows with code, model, and project repositories: Project repository and GitHub integration of HPE Ezmeral ML Ops provides source control, eases collaboration, and enables lineage tracking for improved auditability. The model registry stores multiple models—including multiple versions with metadata—for various runtime engines in the model registry.
Security and control Secure multitenancy with integration to enterprise authentication mechanisms: HPE Ezmeral ML Ops software provides multitenancy and data isolation to ensure logical separation between each project, group, or department within the organization. HPE Ezmeral ML Ops integrates with enterprise security and authentication mechanisms such as LDAP, Active Directory, and Kerberos.
Hybrid deployment On-premises, public cloud, or hybrid: HPE Ezmeral ML Ops runs on-premises on any infrastructure, on public clouds, or in a hybrid model, providing effective utilization of resources and lower operating costs.

License Information

HPE Ezmeral ML Ops must be licensed using an HPE Ezmeral ML Ops license, which entitles a maximum number cores that can be assigned to the quota of all ML Ops tenants.

In addition, the HPE Ezmeral ML Ops license includes the features and products that are part of the HPE Ezmeral Runtime Enterprise license, and many of the applications and features that are included in the HPE Ezmeral Runtime Analytics for Apache Spark license.

Information about the features included with an HPE Ezmeral ML Ops license, with a comparison to other HPE Ezmeral Runtime Enterprise product licenses, is provided in the product QuickSpecs. See What's Included.

More Information

Documentation for users and administrators
HPE Ezmeral ML Ops
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