The two main workflows of machine learning are, first, train the model, then deploy the model. The time it takes to go from a Jupyter notebook to a deployed model in production can be months.The tooling around the training workflow is getting better but deploying is still cumbersome.That is why Kubeflow was created.The KubeFlow project is dedicated to making deployments of machine learning workflows on Kubernetes simple, portable and scalable. In this presentation, we are going to see why such a project exists and the challenges Machine Learning Operations (MLOps) brings to the table.