Deployment of ML Models using Kubeflow on Different Cloud Providers

• URL: http://arxiv.org/abs/2206.13655v1
• Date: Mon, 27 Jun 2022 22:46:11 GMT
• Title: Deployment of ML Models using Kubeflow on Different Cloud Providers
• Authors: Aditya Pandey, Maitreya Sonawane, Sumit Mamtani
• Abstract summary: We create end-to-end Machine Learning models on Kubeflow in the form of pipelines. We analyze various points including the ease of setup, deployment models, performance, limitations and features of the tool.
• Score: 0.17205106391379021
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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