论文标题
使用KubeFlow在不同的云提供商上使用ML模型部署
Deployment of ML Models using Kubeflow on Different Cloud Providers
论文作者
论文摘要
该项目旨在使用称为kubeflow [1]的开源工具（端到端ML ML堆栈编排工具包）探索在Kubernetes上部署机器学习模型的过程。我们以管道的形式创建端到端的机器学习模型，并分析各个点，包括易于设置，部署模型，性能，限制，限制和功能。我们希望我们的项目几乎像一个研讨会/入门报告一样，可以帮助Vanilla Cloud/Kubernetes用户对KubeFlow零知识使用KubeFlow使用KubeFlow来部署ML模型。从不同的云上的设置到通过互联网服务我们训练的模型 - 我们提供详细信息和指标，详细介绍KubeFlow的性能。
This project aims to explore the process of deploying Machine learning models on Kubernetes using an open-source tool called Kubeflow [1] - an end-to-end ML Stack orchestration toolkit. We create end-to-end Machine Learning models on Kubeflow in the form of pipelines and analyze various points including the ease of setup, deployment models, performance, limitations and features of the tool. We hope that our project acts almost like a seminar/introductory report that can help vanilla cloud/Kubernetes users with zero knowledge on Kubeflow use Kubeflow to deploy ML models. From setup on different clouds to serving our trained model over the internet - we give details and metrics detailing the performance of Kubeflow.
