Simplifying AI Application Management with Kubernetes: The Rafay Advantage

Artificial Intelligence (AI) applications have revolutionized industries, but managing and scaling these complex applications – and the large language models (LLMs) powering them – can be a daunting task. This is where Rafay Systems steps in, offering a unified platform from which enterprises can streamline and automate the lifecycle of AI applications and their dependencies, enabling them to unlock the full potential of their AI initiatives.

In this blog post, we will delve into the powerful capabilities of the Rafay Kubernetes Operations Platform and explore how it empowers organizations to automate their infrastructure and operations workflows for AI applications.

Rafay Empowers Enterprises to Scale Their AI Initiatives

The world of ChatGPT, OpenAI, and LLMs in AI is moving fast and it’s imperative that your company leverage the benefits before your competition. Building AI-powered applications is one thing, but the infrastructure setup and maintenance of these AI applications across your infrastructure is another (that’s why OpenAI runs Kubernetes). Rafay makes this easy with GPU integration, unified provisioning, lifecycle management, and monitoring of AI applications no matter where they reside. By leveraging Rafay to manage your AI/ML Applications, you can:

Single Pane of Glass Management Across Public Clouds, Data Centers & Edge

Manage your entire fleet of AI/ML applications from a single pane of glass – across AWS, Azure, GCP (and others), in your on-premises data centers, and at the edge. Leverage a single, consistent GPU-specific dashboard to deploy (from Nvidia, etc.), view and manage clusters and workloads across all your infrastructure.

Provide a Self-Service Experience for Engineers and Data Scientists

With Rafay’s integration with Backstage and other tools, allow your engineers and data scientists to deploy, view, and monitor all of your AI workloads and clusters in any environment, in any region.

Deliver World-Class Security and Governance

As AI/ML goes mainstream, Platform teams find themselves having to demonstrate that they are operating with world-class security and governance. With Rafay, enterprises enforce standards, RBAC, and have an end-to-end audit trail of all actions performed on Kubernetes clusters running LLM-based applications, for example.

Accelerate Your Migration to Artificial Intelligence (AI) Applications

Do you have a deadline by which you need to deploy AI/ML applications? With Rafay, your AI/ML clusters and LLM workloads will be up and running in days and your apps will be deployed in even less time.

 

Key Features for Kubernetes Operations for AI/ML Applications

With Rafay, you have one console to manage the operations of all your AI/ML applications (including LLMs) without having to install custom software, operational processes or dashboards. Key features and capabilities of the Rafay Kubernetes Operations Platform include:

Integrated GPU and Kubernetes Metrics

Rafay automatically captures and aggregates both Kubernetes and GPU metrics at the controller in a multi-tenant time series database. These metrics are then made available to users when they log in, governed by RBAC.

Unified Management of AI/ML Apps

Organizations require a unified, central management platform for all AI/ML clusters in use spanning both data center, cloud-based and edge environments. Rafay acts as a single pane of glass to manage the deployment and lifecycle of all your AI and LLM applications.

Secure Remote Access

Users with very different roles and responsibilities (i.e. data scientists, operations, FinOps, security, contractor, 3rd party ISVs) need access and visibility into the health metrics for the underlying compute, storage infrastructure, GPUs, and their applications.

Cluster and Workflow Standardization

Rafay’s Cluster Blueprints creates and manages version-controlled standards fleet-wide for core components and software add-ons that are deployed on AI/ML clusters.

Multitenancy for AI/ML Apps

It is incredibly common for enterprises to have different teams share clusters – perhaps with specific LLM resources – in an effort to save costs. Rafay’s multi-modal multi-tenancy capabilities can easily support multiple AI/ML teams on the same Kubernetes cluster.

Leverage Rafay to Accelerate AI Initiatives

Rafay Systems revolutionizes AI application management by leveraging the power of Kubernetes. With integrated GPU metrics, unified management, secure remote access, cluster and workflow standardization, and multi-tenancy – all for AI/ML applications, Rafay enables enterprises to leverage the power of AI faster, thus accelerating their AI initiatives.

Read more on this topic by visiting Solutions for Key Kubernetes Challenges for AI/ML in the Enterprise – Part 1 and Part 2.