HPE Accelerates AI Innovation for Managing Entire Machine Learning Lifecycle
Hewlett Packard Enterprise (HPE) today 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 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.
HPE ML Ops transforms AI initiatives from experimentation and pilot projects to enterprise-grade operations and production by addressing the entire machine learning lifecycle from data preparation and model building, to training, deployment, monitoring, and collaboration.
Ritu Jyoti, program vice president, Artificial Intelligence (AI) Strategies at IDC said, “From retail to banking to manufacturing to healthcare and beyond, virtually all industries are adopting or investigating AI/ML to develop innovative products and services and gain a competitive edge. While most businesses are ramping up on the build and train phase of their AI/ML projects, they are struggling to operationalize the entire ML lifecycle from PoC to pilot to production deployment and monitoring.”
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