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What we covered
Getting a model to answer is a weekend. Getting it to answer at volume, at a cost you can defend, is the work. This session is about the second one.
- Usage patterns, and how the shape of your traffic decides everything downstream.
- Managed AI services and Model Garden: what you give up and what you stop maintaining.
- Vector stores in the cloud, and choosing one against your retrieval pattern rather than a benchmark.
- MLOps and LLMOps: what carries over from the former, and what genuinely doesn't.
- Where AI meets Kubernetes, and the Kubeflow architecture underneath it.
- Serving LLMs on Kubeflow, end to end.
- AI agents, and what they add to the inference bill.
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