OPERATIONALISE MACHINE LEARNING AT ENTERPRISE SCALE

HPE Ezmeral ML Ops standardises 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.

HPE GreenLake for ML Ops
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 containerised 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 containerised 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.

Collaboration
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 organisation. 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 utilisation of resources and lower operating costs.

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

HPE ML OPS TECHNICAL VIDEOS

HPE EZMERAL ML OPS PRODUCT DETAILS

HPE Ezmeral ML Ops overcomes “last mile” challenges with a platform that delivers a cloud-like experience, combined with pre-packaged tools, to operationalise the machine learning life cycle 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 life cycle by implementing DevOps-like processes to standardise 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.

SUCCESS IN ACTION
Optum logo

ADVISORY BOARD (OPTUM) USES HPE EZMERAL TO ACCELERATE BUSINESS OUTCOMES WITH AI AND ML IN THE ENTERPRISE

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.

Challenge

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

Solution

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 containerised 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 organisation, and we look forward to continuing our journey together.”

Ramesh Thyagarajan, Executive Director, Advisory Board (Optum)

Result

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

Watch the Video