Rafay Systems, a leading platform provider for Cloud and Kubernetes Automation, has launched an innovative initiative to expedite the deployment of Generative AI applications for enterprise platform teams. The company’s latest offering, GenAI Infrastructure Templates, promises to revolutionize the landscape of AI applications.
Streamlining GenAI Technology Development
Rafay’s GenAI templates provide a robust framework for platform teams to seamlessly guide GenAI technology development and utilization. GenAI has developed templates for AI applications, including reference source code, pre-built cloud environment templates, and Kubernetes cluster blueprints, to meet the growing demand in the business landscape.
Key Features of Rafay’s GenAI Templates
- AI/ML Ecosystem Support: Out-of-the-box compatibility with leading providers, including Amazon Bedrock, Microsoft Azure OpenAI, and OpenAI’s ChatGPT.
- Any Orchestration, Any Cloud: Pre-built templates for Amazon ECS, EKS/A, Microsoft AKS, and Google GKE enable efficient AI resource management across public clouds, private data centers, and edge locations.
- Secure RBAC and Multitenancy: Robust Role-Based Access Control (RBAC) ensures secure environment management, allowing developers, data scientists, and researchers to operate in isolation.
- Integrated GPU and Kubernetes Metrics: Automatic capture and aggregation of Kubernetes and GPU metrics. This provides valuable insights for efficient resource utilization.
Developers and data scientists attending Kubecon 2023 in Chicago witness live demonstrations at booth C31. Rafay’s Git Repository and Documentation provide access to templates, reference designs, and sample code. According to Gartner, by 2026, over 80 percent of enterprises will have embraced generative AI APIs and models.
Rafay’s GenAI Infrastructure Templates mark a significant step in simplifying AI application development. With the ability to deploy enterprise-grade AI development sandboxes at the click of a button, Rafay empowers platform teams, developers, and data scientists to unlock the full potential of AI.