Description
Red Hat OpenShift AI is designed to be a flexible hybrid cloud platform that combines MLOps, GenAIOps, and AgentOps capabilities to facilitate the rapid deployment of agentic AI applications. This platform allows organizations to build, deploy, and monitor AI models and applications efficiently, ensuring that they can leverage the power of AI across different environments, whether on-premise, at the edge, or in disconnected settings.
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The platform supports a variety of open-source tools such as PyTorch, Kubeflow, MLflow, and vLLM, enabling teams to experiment, serve, and deliver AI models at scale. With Red Hat OpenShift AI, organizations can manage costs, data privacy, and agent lifecycles effectively, providing a stable foundation for MLOps. The platform also enhances collaboration among data scientists, AI engineers, and developers, allowing them to work together seamlessly within a single AI application environment.
Security is a critical aspect of Red Hat OpenShift AI, which offers features to filter toxic, biased, or off-topic inputs and outputs, ensuring fairness and safety in AI deployments. The platform includes tools like real-time NeMo guardrails and adversarial vulnerability scanning to protect models from potential threats. Additionally, Red Hat OpenShift AI provides a robust infrastructure for managing AI workloads, streamlining workflows, and ensuring compliance with organizational standards.
Organizations looking to adopt AI can benefit from the extensive resources available through Red Hat, including consulting services, training, and a supportive community. The platform's ability to integrate with major cloud providers further enhances its flexibility, making it a suitable choice for enterprises aiming to innovate and scale their AI initiatives.
- CategoryMachine Learning Platforms
- Pricing ModelSubscription
- DeploymentHybrid Cloud
- PlatformsWeb
Red Hat OpenShift AI's Core Features
Agentic AI: Implement AgentOps for agentic AI, using MLflow.
vLLM: Manage costs of gen AI inference by using GPU resources efficiently.
MCP servers: Actively govern and translate external tool access.
Private AI: Provides on-premise and disconnected mode support.
Models-as-a-Service: Host and provide models for internal use.
llm-d: Optimize complex LLMs at scale in an open source framework.
AI hub: Performance insights from third-party model validation.
Gen AI studio: Hands-on environment to interact with models.
How to use Red Hat OpenShift AI?
Configure: Set up your Red Hat OpenShift AI environment.
Deploy: Use the platform to deploy AI models and applications.
Monitor: Continuously monitor the performance of your AI solutions.
Optimize: Utilize tools for optimizing AI workloads and inference.
Red Hat OpenShift AI's Use Cases
- AI Model Deployment
- Cost Management
- Data Privacy
- Collaborative Development
- Security Management
