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Kubeflow is an open‑source, CNCF‑incubated project that provides a Kubernetes‑native MLOps platform integrating notebooks, training operators, AutoML (Katib), pipelines, and model serving (KServe) to streamline the development, deployment, and scaling of machine learning models across diverse frameworks.
This article explains how a unified AI platform enables rapid, data‑driven creation, training, deployment, and evaluation of OCR models for visually distinct text regions such as seals, meter readings, license plates, and VIN codes, while minimizing hardware and annotation costs.
Yanxuan built a Kubeflow‑based machine‑learning platform that unifies data preprocessing, feature engineering, model training, validation, and deployment, using Smart‑jobs, Smart‑Infer, Smart‑backend, Airflow pipelines, Jupyter notebooks, and Istio‑enhanced inference services to boost algorithm engineers’ efficiency and integrate with Kubernetes, HDFS, and Hive.
This article describes how to collect, clean, and analyze 90,000+ GitHub issues and pull requests from the Kubernetes repository using Kubeflow, TensorFlow, and a fully automated CI/CD pipeline, then build, train, and serve a simple MLP model that classifies release‑note texts as bugs or non‑bugs.
This article explains how MXNet supports data‑parallel training on single‑machine multi‑GPU and multi‑machine multi‑GPU setups, describes KVStore modes, outlines the worker‑server‑scheduler architecture, and shows how to launch large‑scale distributed training using Kubeflow and the mxnet‑operator.
Learn how to perform single‑machine multi‑GPU and multi‑node multi‑GPU training with MXNet, understand KVStore modes, configure workers, servers, and schedulers, and deploy large‑scale distributed training on Kubernetes using Kubeflow, including operator installation, task creation, and performance considerations.
This article introduces Kubeflow, explains the typical machine‑learning model lifecycle, outlines Kubeflow’s core components and Kubernetes advantages, provides detailed server and storage configuration, walks through ksonnet and Kubeflow installation steps, and shows how to verify deployments and access the Kubeflow UI.
This article introduces Kubeflow, a Kubernetes‑based machine‑learning platform, outlines the typical ML lifecycle, details core components, explains why Kubernetes benefits AI workloads, and provides a step‑by‑step guide for installing and accessing Kubeflow’s services, concluding with its industry impact.