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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 Container Platform 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.
HPE Ezmeral Container Platform is a software platform for deploying and managing containerized enterprise applications with 100% open-source Kubernetes at scale—for use cases including machine learning, analytics, IoT/edge, CI/CD, and application modernization.
Kubernetes has emerged as the de-facto open-source standard for container orchestration and a fundamental building block for cloud-native architectures. However, while it is straightforward to deploy modern, cloud-native applications in containers, these represent a small portion of enterprise applications. The vast majority of enterprise applications are still non-cloud-native or monolithic. The challenge is to deploy and run these monolithic applications in containers, without re-architecting them.
In addition, as enterprise organizations extend the use of containers and Kubernetes beyond development and testing to production environments, they need to address key considerations including security and data persistence.
Are your deployments of distributed AI and analytics applications complex and time-consuming?
Does it take weeks or even months for large-scale enterprise implementation on infrastructure that you aren’t certain has the capabilities to support your objectives? Deploying HPE Apollo Systems for BlueData EPIC Software, based on the HPE Elastic Platform for Analytics (EPA) architecture, provides an efficient, flexible and cost effective solution addressing the evolving requirements of these workloads. This cloud-ready infrastructure can be seamlessly extended in a contiguous hybrid cloud with Amazon Web Services, Google Cloud Platform, and Microsoft Azure. The logical and physical separation of compute and storage enables you to increase efficiency and flexibility. HPE Apollo Systems for BlueData Software helps eliminate the need for a dedicated DevOps team by provisioning and configuring components, facilitating cloud deployments, and jump-starting your AI and analytics initiatives..
Are your on-premises deployments of distributed AI and Analytics applications complex and time-consuming?
Does it take weeks or even months for large-scale enterprise implementation with bare-metal infrastructure? BlueData EPIC (Elastic Private Instant Clusters) software platform gives you the ability to quickly, easily, and cost-effectively deploy applications, regardless of the infrastructure, in self-service, elastic, automated, and secure environments. This software can create distributed AI, Machine Learning (ML), and Big Data Analytics environments in minutes rather than months, whether on-premises, in multiple public clouds, or in a hybrid model, allowing you to quickly respond to dynamic business requirements in a variety of use cases. BlueData EPIC software offers flexibility and agility within a seamless infrastructure that is invisible to your end user community of data scientists, analysts, and developers.