KCD Nigeria 2026: Telling the African Cloud Native Story

Opening/Intro

AI Workspaces for the Modern Era – Kubeflow Notebooks v2

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 on Kubeflow

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…

Automatic Resource Configuration for Kubeflow TrainJobs

Currently, Kubeflow TrainJob requires users to manually specify GPU, CPU, and memory requests before submission. Getting the resource requirements right is crucial: - Under-request…

Kubeflow Community Distribution

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…

Break

Interactive Spark at Your Fingertips: Integrating SparkConnect into Kubeflow Notebooks

Background Apache Spark is indispensable for large-scale data processing and feature engineering in ML pipelines. However, running Spark interactively within a Kubeflow Notebooks e…

Closing the Observability Gap: Building a Kubeflow Plugin for Headlamp

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…

Beyond 'Agent Works', Testing AI Agents and MCP Servers in Production

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…