When your data science team sets out to build a new generative AI application, they face a flood of critical decisions: Which model should they use? Which vector database is right for RAG? How can they get access to secure, scalable compute? Without a clear strategy, these challenges lead to delays, security risks, and duplicated effort across teams.
Join Cansu Kavili Oernek, an AI Platform Architect at Red Hat, as she explains how a dedicated platform engineering approach solves these problems. Using a powerful concert analogy, Cansu demonstrates how platform engineers act as the "stage crew" for your data scientists, providing the common services, guardrails, and infrastructure they need.
In this video, youβll learn how platform engineering helps by:
– Providing a safe, self-service space for experimentation.
– Delivering auto-scaling compute to manage resources and costs effectively.
– Building in security and compliance guardrails from the start.
Learn how to empower your AI teams to focus on innovation by letting a platform team handle the logistics.
Explore how Red Hat enables platform engineering for AI at scale:
βοΈ Learn about Red Hat OpenShift AI β https://www.redhat.com/en/technologies/cloud-computing/openshift/openshift-ai
π Read the OpenShift overview on building a distributed AI platform β https://www.redhat.com/en/resources/openshift-ai-overview
β¨ Discover more about Platform Engineering β https://www.redhat.com/en/topics/devops/platform-engineering
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