HPE accelerates Artificial Intelligence innovation with enterprise-grade solution for managing entire machine learning lifecycle

Hewlett Packard Enterprise (HPE) announced a container-based software solution, HPE ML Ops, to support the entire machine learning model lifecycle for on-premises, public cloud and hybrid cloud environments. The new solution introduces a DevOps-like process to standardize machine learning workflows and accelerate AI deployments from months to days.

The new HPE ML Ops solution extends the capabilities of the BlueData EPIC™ container software platform, providing data science teams with on-demand access to containerized environments for distributed AI / ML and analytics. BlueData was acquired by HPE in November 2018 to bolster its AI, analytics, and container offerings, and complements HPE’s Hybrid IT solutions and HPE Pointnext Services for enterprise AI deployments.

Enterprise AI adoption has more than doubled in the last four years1, and organizations continue to invest significant time and resources in building machine learning and deep learning models for a wide range of AI use cases such as fraud detection, personalized medicine, and predictive customer analytics. However, the biggest challenge faced by technical professionals is operationalizing ML, also known as the “last mile,” to successfully deploy and manage these models, and unlock business value. According to Gartner, by 2021, at least 50 percent of machine learning projects will not be fully deployed due to lack of operationalization.

With the HPE ML Ops solution, data science teams involved in building and deploying ML models can benefit from the industry’s most comprehensive operationalization and lifecycle management solution for enterprise AI:

• Model Build: Pre-packaged, self-service sandbox environments for ML tools and data science notebooks
  
  
• Model Training: Scalable training environments with secure access to data
  
  
• Model Deployment: Flexible and rapid deployment with reproducibility
  
  
• Model Monitoring: End-to-end visibility across the ML model lifecycle
  
  
• Collaboration: Enable CI/CD workflows with code, model, and project repositories
  
  
• Security and Control: Secure multi-tenancy with integration to enterprise authentication mechanisms
  
  
• Hybrid Deployment: Support for on-premises, public cloud, or hybrid cloud

The source of information: https://www.hpe.com/us/en/newsroom/press-release/2019/09/hpe-accelerates-artificial-intelligence-innovation-with-enterprise-grade-solution-for-managing-entire-machine-learning-lifecycle.html