The kcp project is reimagining Kubernetes as a scalable, multi-tenant control plane for any API. Whether you're building an Internal Developer Platform, a SaaS offering, or exploring the future of Kubernetes, this half-day virtual event will explore Workspaces, API Exports, and multi-tenancy with insights from kcp maintainers and community members.
Kubeflow Notebooks runs interactive development environments for AI, ML, and Data workloads on Kubernetes. In this talk, we are pleased to announce that Kubeflow Notebooks 2.0 is n…
Agentic RAG extends traditional retrieval-augmented generation by enabling AI systems to plan, use tools, evaluate intermediate results, and refine responses through multi-step wor…
Currently, Kubeflow TrainJob requires users to manually specify GPU, CPU, and memory requests before submission. Getting the resource requirements right is crucial: - Under-request…
The goal of this session is to first introduce the new Kubeflow community distribution working group and committee. Who is involved, how to participate. Furthermore I want to explo…
Apache Spark is indispensable for large-scale data processing and feature engineering in ML pipelines. However, running Spark interactively within a Kubeflow Notebooks environment …
Managing ML workloads on Kubernetes means constant context-switching toggling between Headlamp and Kubeflow's UI just to understand what's running in your cluster. The Kubeflow Hea…
AI agents and MCP servers introduce a critical untested layer. While we rigorously test APIs and models, agent behavior, tool selection and MCP server interactions often goes unval…
An audience of 13 companies, parsed from the program.
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