GitOps and Multi-Tenancy Combined for an Enterprise Data Science Experience on Kubeflow
GitOps and Multi-Tenancy Combined for an Enterprise Data Science Experience on Kubeflow


Kubeflow is a machine learning (ML) platform built on top of Kubernetes. The Kubeflow project is dedicated to making deployments of ML workflows on Kubernetes simple, portable and scalable.

GitOps is the methodology of defining all infrastructure as declarative code and tracking it using git. In Kubeflow and Kubernetes, GitOps is a best practice to achieve immutable, reproducible infrastructure that can scale according to an organization’s needs.

In this session, you will: 1) learn how to apply GitOps in order to deploy and manage a Kubeflow cluster; 2) learn how to enable multiple users to work together on the same cluster in a secure and isolated way, with authentication and authorization best practices; 3) follow a data scientist’s journey to running a hyperparameter tuning optimization workflow; 4) scale up your workloads in a UI driven environment.

Session Outline
* Lesson 1: GitOps and Declarative Infrastructure

Revisit the declarative nature of Kubernetes and apply GitOps best practices to get immutable, trackable and reproducible infrastructure. Deploy and manage Kubeflow using the GitOps methodology.

* Lesson 2: Multi-User Kubeflow

Learn how Kubeflow and Kubernetes enforce authentication and authorization. Then see this knowledge applied in practice in order to enable multiple users to share the same Kubeflow cluster in a secure and isolated manner.

* Lesson 3: Secure and Isolated User Workflows

Follow the steps of a data scientist deploying their pipelines in a secure and isolated manner. Learn how secrets are securely distributed and injected into the user’s environment. Try out an end-to-end user workflow right out of your Jupyter Notebook, by leveraging Kale, the easiest way to go from Notebook to Pipeline.

Background Knowledge
Attendees should be familiar with Kubernetes.


Stefano Fioravanzo is a Software Engineer at Arrikto, his background is in Data Science and ML Research. He understands the value of building robust Machine Learning infrastructure and providing Data Scientists with the necessary tools to scale up their workflows. He works as a full-time contributor to Kubeflow and he is the creator of the Kubeflow Kale project which enables Jupyter Notebooks deployments to Kubeflow Pipelines.

Open Data Science




Open Data Science
One Broadway
Cambridge, MA 02142

Privacy Settings
We use cookies to enhance your experience while using our website. If you are using our Services via a browser you can restrict, block or remove cookies through your web browser settings. We also use content and scripts from third parties that may use tracking technologies. You can selectively provide your consent below to allow such third party embeds. For complete information about the cookies we use, data we collect and how we process them, please check our Privacy Policy
Consent to display content from - Youtube
Consent to display content from - Vimeo
Google Maps
Consent to display content from - Google