  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.
