OPERATIONALIZE MACHINE LEARNING AT ENTERPRISE SCALE
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
- Model Training
- Model Deployment and Monitoring
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.
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.
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.
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.
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 profitability
- 52%better customer experience
- 49%better adoption of data science best practices
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.
“Our online games generate billions of data points every day. Using complex ML models, our data scientists leverage this data for prescriptive analytics to improve our players’ experience, lifetime value, and loyalty. With HPE Ezmeral software, we’re containerizing these ML and analytics environments to help improve operational efficiency and optimize our business.”Alex Ryabov, Head of Data Services, Wargaming
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 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.
ML OPS ON-DEMAND LEARNING
Learn HPE Ezmeral ML Ops through on-demand courses that provide Artificial Intelligence (AI) and Machine Learning (ML) foundational knowledge as well as hands-on technical experience. With only an estimated 20% of ML projects making it into production, learn the basic concepts of AI and ML, and how learning algorithms work.