Popular GitHub Repositories for Kubeflow

Machine Learning Toolkit for Kubernetes

A guideline for building practical production-level deep learning systems to be deployed in real world applications.

cube studio开源云原生一站式机器学习/深度学习/大模型AI平台，支持sso登录，大数据平台对接，notebook在线开发，拖拉拽任务流pipeline编排，多机多卡分布式训练，超参搜索，推理服务VGPU，边缘计算，标注平台，自动化标注，大模型微调，vllm大模型推理，llmops，私有知识库，AI模型应用商店，支持模型一键开发/推理/微调，支持国产cpu/gpu/npu芯片，支持RDMA，支持pytorch/tf/mxnet/deepspeed/paddle/colossalai/horovod/spark/ray/volcano分布式，deepseek训练推理

Standardized Serverless ML Inference Platform on Kubernetes

PipelineAI

Machine Learning Pipelines for Kubeflow

Elyra extends JupyterLab with an AI centric approach.

Distributed ML Training and Fine-Tuning on Kubernetes

Automated Machine Learning on Kubernetes

Introducing DeToxify – the ultimate toxicity buster. Armed with NLP superpowers, it sniffs out harmful comments, neutralizes negativity, and rewrites toxicity into pure class. Think of it as Jarvis for your online spaces—making the internet a kinder, smarter place, one comment at a time.

DoEKS is a tool to build, deploy and scale Data & ML Platforms on Amazon EKS

Distributed Machine Learning Patterns from Manning Publications by Yuan Tang https://bit.ly/2RKv8Zo

deployKF builds machine learning platforms on Kubernetes. We combine the best of Kubeflow, Airflow?, and MLflow? into a complete platform.

Compare MLOps Platforms. Breakdowns of SageMaker, VertexAI, AzureML, Dataiku, Databricks, h2o, kubeflow, mlflow...

Cloud Native ML/DL Platform

:snowflake: :whale: Awesome tools and libs for AI, Deep Learning, Machine Learning, Computer Vision, Data Science, Data Analytics and Cognitive Computing that are baked in the oven to be Native on Kubernetes and Docker with Python, R, Scala, Java, C#, Go, Julia, C++ etc

A lightweight tool to get an AI Infrastructure Stack up in minutes not days. K3ai will take care of setup K8s for You, deploy the AI tool of your choice and even run your code on it.

This repo covers Kubeflow Environment with LABs: Kubeflow GUI, Jupyter Notebooks on pods, Kubeflow Pipelines, Experiments, KALE, KATIB (AutoML: Hyperparameter Tuning), KFServe (Model Serving), Training Operators (Distributed Training), Projects, etc.

Hands-on MLOps projects to explore and learn the practical aspects of machine learning engineering for production.

This repository contains Dockerfiles, scripts, yaml files, Helm charts, etc. used to scale out AI containers with versions of TensorFlow and PyTorch that have been optimized for Intel platforms. Scaling is done with python, Docker, kubernetes, kubeflow, cnvrg.io, Helm, and other container orchestration frameworks for use in the cloud and on-premise