# Kubeflow The Machine Learning Toolkit for Kubernetes. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Kubeflow Has Graduated from CNCF DevFeed: [Kubeflow Has Graduated from CNCF]() Original publisher: [Read original article]() Author: Kubeflow Published: 2026-08-18T05:00:00Z Content type: release Language: en Sources: [Kubeflow]() Topics: [AI Infrastructure](), [AI Platform](), [Kubernetes](), [Cloud Native Ecosystem](), [distributed-training](), [Fine-tuning](), [model-serving]() Tags: [ai-infrastructure](), [announcement](), [cloud-native](), [cncf](), [distributed-training](), [fine-tuning](), [inference](), [kubernetes](), [model-serving](), [production]() ### AI overview Kubeflow has graduated from the Cloud Native Computing Foundation, recognizing its maturity and adoption as a Kubernetes-native platform for AI and machine learning workloads. The article highlights its community growth, enterprise use, security audit, governance work, and focus on scalable, portable infrastructure. ### Source excerpt Kubeflow is a CNCF Graduated Project ## Introducing Kubeflow MCP: An Agent Interface for Cloud Native AI at Scale DevFeed: [Introducing Kubeflow MCP: An Agent Interface for Cloud Native AI at Scale]() Original publisher: [Read original article]() Author: Abhijeet Dhumal Published: 2026-08-10T05:00:00Z Content type: article Language: en Sources: [Kubeflow]() Topics: [Model Context Protocol](), [Artificial Intelligence](), [AI Infrastructure](), [MCP Server](), [Cloud](), [Kubernetes]() Tags: [agent](), [ai](), [ai-infrastructure](), [cloud](), [cloud-native](), [kubernetes](), [mcp](), [mcp-server](), [release]() ### AI overview The article introduces the Kubeflow MCP Server, an interface that lets AI agents interact directly with Kubeflow infrastructure through Model Context Protocol tools. It describes approval-based workflows for inspecting clusters, estimating resources, previewing and submitting training jobs, and streaming logs. ### Source excerpt An open MCP interface that lets AI agents operate Kubeflow directly from conversation. ## KubeCon + CloudNativeCon India 2026: Our Kubeflow Community Experience DevFeed: [KubeCon + CloudNativeCon India 2026: Our Kubeflow Community Experience]() Original publisher: [Read original article]() Author: Khushi Agrawal Published: 2026-07-27T05:00:00Z Content type: article Language: en Sources: [Kubeflow]() Topics: [Kubernetes](), [MLOps](), [distributed-training](), [model-serving](), [Large Language Model](), [Retrieval Augmented Generation (RAG)](), [GPU](), [Cloud]() Tags: [2026](), [ai](), [cloud-native](), [community](), [distributed-training](), [gpu](), [india](), [kubecon](), [kubernetes](), [llms](), [machine-learning](), [mlops](), [model-serving](), [model-training](), [rag]() ### 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 ## Batch Jobs for SparkClient: Submitting and Managing Spark Workloads from Python DevFeed: [Batch Jobs for SparkClient: Submitting and Managing Spark Workloads from Python]() Original publisher: [Read original article]() Author: Sameer Yadav Published: 2026-07-25T05:00:00Z Content type: tutorial Language: en Sources: [Kubeflow]() Topics: [Apache Spark](), [SDKs](), [Python](), [Kubernetes]() Tags: [batch](), [cleanup](), [gsoc](), [jobs](), [kubernetes](), [logs](), [pipelines](), [python](), [scheduled](), [sdk](), [spark]() ### AI overview This tutorial explains how the Kubeflow SDK's SparkClient supports submitting and managing batch Spark workloads on Kubernetes from Python. It covers script- and function-based jobs, lifecycle operations, log retrieval, cleanup, and the implementation's current boundaries. ### Source excerpt How the SparkClient SDK's new batch job APIs work under the hood -- submit_job(), FileJob/FuncJob, the lifecycle APIs, and log retrieval. ## Kale 2.0 adds KFP v2 compatibility and modernizes its JupyterLab extension DevFeed: [Kale 2.0 adds KFP v2 compatibility and modernizes its JupyterLab extension]() Original publisher: [Read original article]() Author: Stefano Fioravanzo Published: 2026-05-06T05:00:00Z Content type: opinion Language: en Sources: [Kubeflow]() Topics: [Data Science](), [Open Source](), [SDKs](), [migration](), [Usability](), [Documentation]() Tags: [data-science](), [documentation](), [kale](), [migration](), [open-source](), [release](), [sdk](), [usability]() ### AI overview This opinion article discusses Kale 2.0, a new release of the open-source project now part of the Kubeflow ecosystem. It highlights full Kubeflow Pipelines v2 compatibility, a modernized JupyterLab 4 extension, removal of legacy pieces, usability improvements, refactoring, updated examples, and new documentation. ### Source excerpt This is a post I'd long wished for, then lost hope for, then forgot about. Somehow, things started moving again. And here we are. Kale is officially part of the Kubeflow ecosystem, it's alive and growing, and now has a new release that will lay the foundation for a whole new chapter. ## Kubeflow SDK User Survey 2026 - Feedback, Insights and Roadmap DevFeed: [Kubeflow SDK User Survey 2026 - Feedback, Insights and Roadmap]() Original publisher: [Read original article]() Author: Kubeflow SDK Team Published: 2026-04-28T05:00:00Z Content type: article Language: en Sources: [Kubeflow]() Topics: [SDK](), [Developer experience](), [Orchestration](), [Model Development](), [PyTorch](), [Kubernetes](), [GPU]() Tags: [2026](), [community](), [developer](), [developer-experience](), [gpu](), [insights](), [jupyter-notebook](), [kubernetes](), [orchestration](), [pytorch](), [sdk](), [survey](), [tooling]() ### AI overview The article reports findings from a Kubeflow SDK user survey involving practitioners from the Kubeflow ecosystem. It describes how Kubeflow is used in machine-learning workflows, highlights common tooling and components, and summarizes challenges involving infrastructure complexity, resource management, and debugging. ### Source excerpt To better understand the needs of our community, the Kubeflow SDK working group recently conducted a user survey focused on the SDK and developer workflows. The goal was to gather feedback from practitioners across the ecosystem about their current tooling, common challenges, and the features they would most like to see improved. ## Kubeflow Community Distribution 26.03.1 Release Announcement DevFeed: [Kubeflow Community Distribution 26.03.1 Release Announcement]() Original publisher: [Read original article]() Author: Kubeflow 26.03 Release Team Published: 2026-04-11T05:00:00Z Content type: release Language: en Sources: [Kubeflow]() Topics: [releases](), [Kubernetes](), [MinIO](), [istio](), [OpenID connect (OIDC)](), [kubectl]() Tags: [announcement](), [authentication](), [authorization](), [catalog](), [ci](), [istio](), [kubectl](), [kubernetes](), [minio](), [network](), [oidc](), [parallel](), [release](), [storage](), [ui](), [updates](), [versioning]() ### AI overview The Kubeflow Community Distribution 26.03.1 release introduces calendar-based versioning and updates components including Kubernetes, Kubeflow Pipelines, KServe, Trainer, notebooks, the dashboard, Istio, and related dependencies. It also adds native OIDC support, conditional and parallel Local Runner control flows, SeaweedFS storage support, and completes MinIO deprecation. ### Source excerpt The release versioning is now calendar-based (Year.Month.Patch). Around two base releases are planned per year with optional patch releases. The best-effort only community support is roughly 6 months and there is commercial support available from multiple vendors. Please update regularly as explained in our upgrading and extending section to benefit also from security and performance improvements. Release details: 26.03 and 26.03.1. ## Modernizing Kubeflow Pipelines UI DevFeed: [Modernizing Kubeflow Pipelines UI]() Original publisher: [Read original article]() Author: Manaswini Das Published: 2026-03-31T05:00:00Z Content type: article Language: en Sources: [Kubeflow]() Topics: [React](), [Front end](), [ui](), [Accessibility](), [Web](), [Testing]() Tags: [accessibility](), [frontend](), [metrics](), [migration](), [navigation](), [performance](), [pipelines](), [react](), [ui](), [visualization](), [web]() ### AI overview Kubeflow Pipelines' web interface was upgraded from React 16 to React 19. The frontend modernization improves responsiveness, pipeline graph navigation, charts, accessibility, and testing foundations while keeping the production bundle size unchanged and preserving user workflows, APIs, backend behavior, pipeline execution, and artifact storage. ### Source excerpt The Kubeflow Pipelines web interface has been upgraded from React 16 to React 19 -- a modernization effort that touches every layer of the frontend stack. Whether you use the UI to manage pipelines day-to-day or contribute to the codebase, here is what this means for you. ## Kubeflow Trainer v2.2: JAX & XGBoost Runtimes, Flux for HPC Support, and TrainJob progress and metrics observability DevFeed: [Kubeflow Trainer v2.2: JAX & XGBoost Runtimes, Flux for HPC Support, and TrainJob progress and metrics observability]() Original publisher: [Read original article]() Author: Kubeflow Trainer Team Published: 2026-03-20T05:00:00Z Content type: release Language: en Sources: [Kubeflow]() Topics: [Kubernetes](), [distributed-training](), [observability](), [flux](), [Fine-tuning]() Tags: [distributed-training](), [fine-tuning](), [flux](), [gpu](), [hugging-face](), [kubernetes](), [observability](), [release](), [trainer]() ### AI overview Kubeflow Trainer v2.2 adds native JAX and XGBoost training runtimes, Flux runtime support for HPC workloads, enhanced training-job observability, timeout policies, and more flexible runtime configuration through new APIs. It also unifies supported training workloads under the TrainJob abstraction and enables distributed JAX workloads on Kubernetes. ### Source excerpt Just a little over one week ahead of KubeCon + CloudNativeCon EU 2026, the Kubeflow team is excited to ship Trainer v2.2. The v2.2 release reinforces our commitment to expanding the Kubeflow Trainer ecosystem - meeting developers where they are by adding native support for JAX, XGBoost, and Flux, while also delivering deeper observability into training jobs. ## Kubeflow SDK v0.4.0: Model Registry, SparkConnect, and Enhanced Developer Experience DevFeed: [Kubeflow SDK v0.4.0: Model Registry, SparkConnect, and Enhanced Developer Experience]() Original publisher: [Read original article]() Author: Kubeflow SDK Team Published: 2026-03-19T05:00:00Z Content type: release Language: en Sources: [Kubeflow]() Topics: [SDKs](), [MLOps](), [Apache Spark](), [data-processing](), [Hyperparameter optimization](), [Machine learning](), [Kubernetes](), [Python]() Tags: [apache-spark](), [data-processing](), [hyperparameter-optimization](), [kubernetes](), [mlops](), [python](), [release](), [sdk]() ### AI overview Kubeflow SDK v0.4.0 introduces a Model Registry Client, SparkClient with SparkConnect support, namespaced TrainingRuntimes, dataset and model initializers, and new documentation. The release targets a unified Python interface for AI workloads on Kubernetes across data processing, model management, and ML pipelines. ### Source excerpt Explore the full documentation at sdk.kubeflow.org