Cray Software

Do you need to streamline the AI/ML deployment process? Do you need to support a diverse AI frameworks and scalable infrastructure in a cloud/hybrid environment that often requires customized data protection?
The HPE Machine Learning Inference Software features user-friendly tools to update, monitor, and deploy models that will help you get value from AI/ML initiatives faster. Role-Based Access Controls (RBAC) and endpoint security provide additional protection for ML resources. Dramatically improve team efficiency by using consistent tooling and pre-trained models to focus more on model development and less on the complexities of getting models into production. By offering a product that handles the intricacies of deployment, routing, and real-time monitoring, HPE Machine Learning Inference Software provides the agility needed to ship ML models quickly, iterate on them based on feedback from the real-world, and maintain high-performance standards.

How do you enable a system to perform like a supercomputer, but run like a cloud?

HPE Cray System Management for HPE Cray supercomputers is a solution enabling system administrators to manage large-scale supercomputers leveraging the architecture and advances of hyper-scalers and cloud providers. While offering the familiar capabilities of high performance computing (HPC) system management software, HPE Cray System Management enables customers to go beyond the traditional and enable new services, deploy broad ranges of workloads, and drive towards the as-a-service experience. Built to manage systems scaling to Exascale, HPE Cray System Management offers everything needed for manageability, reliability, and interoperability for your HPE Cray supercomputers.

Do you need to scale your machine learning (ML) and data pipelines when processing large amounts of structured and unstructured data?

HPE Machine Learning Data Management Software delivers a flexible data layer platform that automates complex ML and data pipelines, while providing data versioning and lineage for reproducibility. Increase the performance of your pipelines with autoscaling and parallel processing built on Kubernetes for resource orchestration. Standard object stores, deduplication, and pipelines are automatically triggered when data is modified, thus saving your engineers time and resources. This platform features immutable data lineage with data versioning of any data type to provide traceable results, allowing any outcome to be reproduced.

Are your model development and MLOps teams spending more time setting up and managing ML infrastructure, rather than building and deploying models to production?

HPE Machine Learning Development Environment as a managed service is a fully managed MLOps platform that allows model developers and researchers to focus on building better models faster by reducing complexity and removing the need to write boilerplate code associated with managing ML infrastructure. It easily integrates with ML frameworks and tools, and supports customers bringing their own AWS or GCP cloud environments.

Our platform also makes it easy for IT and MLOps teams to setup and share AI infrastructure to improve collaboration and productivity for ML teams, while reducing costs.

Train models faster, build more accurate models, efficiently manage and share AI infrastructure, and track and reproduce experiments easily with HPE Machine Learning Development Environment as a managed service.