Abstract: 

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.

Bio: 

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.