HPE Machine Learning Inference Software Base 1-year E-RTU

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.

What is new supporting point:
Create a simplified path to scalable production model deployments for MLOps or ITOps, using an intuitive graphical interface removing the need for extensive Kubernetes experience.
Streamlined integration with Hugging Face and NVIDIA Foundation Models offers a zero-coding deployment experience for large language models (LLMs) directly from Hugging Face and NVIDIA NGC.
Seamless integration with NVIDIA AI Enterprise includes NIM microservices for enhanced inference on more than two dozen popular AI models from NVIDIA and partners.
Facilitate support for pre-trainied and bespoke models built on popular frameworks such as TensorFlow, PyTorch, scikit-learn, and XGBoost.
Benefit from integrated monitoring and logging for tracking model performance, usage metrics, and system health, facilitating proactive optimization.
Offer adaptable deployment across varied infrastructures with compatibility for many Kubernetes environments, including HPE Ezmeral, HPE GreenLake, AWS, Azure, Google Cloud, and on-premise setups.