# KubeCon + CloudNativeCon India 2026: Our Kubeflow Community Experience

DevFeed: [KubeCon + CloudNativeCon India 2026: Our Kubeflow Community Experience](<https://devfeed.tech/articles/kubecon-cloudnativecon-india-2026-our-kubeflow-community-experience-17608.md>)

Original publisher: [Read original article](<https://blog.kubeflow.org/kubecon/community/2026/07/27/kubecon-2026-india-kubeflow.html>)

Author: Khushi Agrawal

Published: 2026-07-27T05:00:00Z

Content type: article

Language: en

Sources: [Kubeflow](<https://devfeed.tech/sources/kubeflow.md>)

Topics: [Kubernetes](<https://devfeed.tech/topics/kubernetes.md>), [MLOps](<https://devfeed.tech/topics/mlops.md>), [distributed-training](<https://devfeed.tech/topics/distributed-training.md>), [model-serving](<https://devfeed.tech/topics/model-serving.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Cloud](<https://devfeed.tech/topics/cloud.md>)

Tags: [2026](<https://devfeed.tech/tags/2026.md>), [ai](<https://devfeed.tech/tags/ai.md>), [cloud-native](<https://devfeed.tech/tags/cloud-native.md>), [community](<https://devfeed.tech/tags/community.md>), [distributed-training](<https://devfeed.tech/tags/distributed-training.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [india](<https://devfeed.tech/tags/india.md>), [kubecon](<https://devfeed.tech/tags/kubecon.md>), [kubernetes](<https://devfeed.tech/tags/kubernetes.md>), [llms](<https://devfeed.tech/tags/llms.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [mlops](<https://devfeed.tech/tags/mlops.md>), [model-serving](<https://devfeed.tech/tags/model-serving.md>), [model-training](<https://devfeed.tech/tags/model-training.md>), [rag](<https://devfeed.tech/tags/rag.md>)

## AI overview

A Kubeflow community recap of KubeCon + CloudNativeCon India 2026 in Mumbai. It describes Kubeflow's presence at the CNCF Project Pavilion, common architecture questions, featured talks, booth discussions about production machine learning workflows, distributed training, model serving, GPU resource allocation, LLMs, and RAG pipelines, and efforts to onboard contributors.

## Source excerpt

Introduction