OPERATIONALIZE MACHINE LEARNING AT ENTERPRISE SCALE
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
- Model Training
- Model Deployment and Monitoring
- Security and Control
- Hybrid Deployment
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.
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 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.
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 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.
- 53increased profitability 1
- 52better customer experience 2
- 49better adoption of data science best practices 3
Now available on-demand, see how to run, manage, control and secure apps, data and IT operations that run your business. Learn how HPE Ezmeral software runs containers and Kubernetes at scale to modernize apps, from edge to cloud.
“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
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.
HPE Ezmeral ML Ops
A software solution that extends the capabilities of the HPE Ezmeral Container platform to support the entire machine learning lifecycle and implement DevOps like processes to standardize machine learning workflows.
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.
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.”
Data-driven insights needed to improve operational efficiency, reduce infrastructure costs, and enhance patient care.
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.