Forrester: Operationalize Machine Learning
Forrester: Operationalize Machine Learning
Forrester conducted an online survey to understand complexities of machine learning and discover how to leverage ML Ops to deploy machine learning at scale in the enterprise.
Read the Forrester Report


HPE Ezmeral ML Ops provides pre-packaged tools to operationalize machine learning workflows at every stage of the ML lifecycle, from pilot to production, giving you DevOps-like speed and agility.

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devops-like speed and agility for the ML lifecycle

Standardize processes across the ML lifecycle to build, train, deploy, and monitor machine learning models.

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Model Build
Watch Video: HPE Ezmeral Machine Learning Ops
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.

Model Training
Watch Demo: Build and Train a Model
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.

Model Deployment and Monitoring
Watch Demo: Deploy the Model
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.

Watch Demo: Setup Project Repository
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.

Security and Control
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.

Hybrid Deployment
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.

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Technical videos

Faster time to value for AI / ML

HPE provides data science teams with one-click deployment for distributed AI / ML environments and secure access to the data they need.

Optum logo


Advisory Board (Optum) deploys predictive analytics and machine learning on big data using the container-based platform from HPE Ezmeral. Learn how they streamlined operations and cut costs while enhancing patient care in U.S. hospitals.


Helping hospitals across the US translate their big data into actionable insights that deliver business value.


Deployment of distributed ML and analytics applications and for the separation of compute and memory from storage.

“HPE Ezmeral has helped us to address these challenges with their containerized solution that has delivered faster time-to-insights, reduced our costs, and freed up our staff to innovate. It’s paying big dividends for our organization, and we look forward to continuing our journey together.”

Ramesh Thyagarajan, Executive Director, Advisory Board (Optum)


Data-driven insights needed to improve operational efficiency, reduce infrastructure costs, and enhance patient care. 

Watch the Video