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Red Hat Certified Developer in AI
A platform engineer is evaluating deployment options for a new AI initiative. The organization requires the ability to deploy open weight models and autonomous agents across on-premise, edge, and disconnected environments while maintaining data sovereignty. Which characteristic of Red Hat OpenShift AI most directly satisfies this requirement?
Explanation
A team wants to reduce the cost of generative AI inference while maintaining stable latency at scale as user demand grows. Which OpenShift AI component is specifically designed to manage these inference costs by using GPU resources efficiently?
Explanation
vLLM helps manage costs of gen AI inference by using GPU resources efficiently. Speed up AI inference with lower cost-per-token and stable latency at scale.
AI hub provides performance insights from third-party model validation. Centrally manage trusted foundational AI assets, including LLMs.
Monitor deployed models for bias, data drift, and performance by using TrustyAI and observability tools
using Elyra and Kubeflow SDK to automate fundamental AI/ML
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