Artificial Intelligence and Analytics

Much like pre-DevOps software development, data science organizations still spend a significant amount of time and effort when moving projects from development to production. Model version control and code sharing is manual, and there is a lack of standardization on tools and frameworks, making it tedious and time-consuming to productize machine learning models.

HPE Ezmeral Machine Learning Ops (HPE Ezmeral ML Ops) extends the capabilities of the HPE Ezmeral Runtime Enterprise and brings DevOps-like agility to enterprise machine learning. With the HPE Ezmeral ML Ops, enterprises can implement DevOps processes to standardize their ML workflows.

HPE Ezmeral ML Ops provides data science teams with a platform for their end-to-end data science needs with the flexibility to run their machine learning or deep learning (DL) workloads on-premises, in multiple public clouds, or a hybrid model and respond to dynamic business requirements in a variety of use cases.

Are business insights and innovation slowed by restricted access to data? HPE Ezmeral Data Fabric securely works around data silos to aggregate different data types and formats into a single data backbone that accelerates analytics and AI without complex ETL processes or copying of data. Customers report faster data management, building of models 75% faster¹ and the ability to handle more types of data and modeling algorithms than ever before.  

Layer on open source tools and frameworks from the certified ecosystem to process data at its point of creation across hybrid, edge, and multicloud environments. 

Empower data teams to search and pinpoint the exact data with metadata and data intelligence from BigID. Truncate data discovery from days/weeks to minutes, identify unprotected sites, then apply remediation policies, encrypt or move data. Ensure that data is being accessed and used to organizational policies.

HPE Ezmeral Runtime Enterprise is an enterprise-grade container orchestration platform that is designed for the containerization of both cloud-native and non-cloud-native monolithic applications with persistent data. It deploys 100% open-source Kubernetes for orchestration, provides a state-of-the-art file system and data fabric for persistent container storage, and provides enterprises with the ability to deploy AI and Analytics workloads in containers. Enterprises can now easily extend the agility and efficiency benefits of containers to more of their enterprise applications—running on either bare-metal or virtualized infrastructure, on-premises, in multiple clouds, or at the edge.