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Webinars

Kubeflow Pipelines on-prem

June 21, 2019

The Taxi Cab (or Chicago Taxi) example is a very popular data science example that predicts trips that result in tips greater than 20% of the fare. This example is already ported to run as a Kubeflow Pipeline on GCP, and included in the corresponding KFP repository. We are going to showcase the Taxi Cab example running locally, using the new MiniKF, and demonstrate Rok’s integration as well.

In this webinar, you will learn how to:
– Easily execute a local/on-prem Kubeflow Pipelines end-to-end example
– Seamlessly integrate Jupyter Notebooks and Kubeflow Pipelines with Arrikto’s Rok
– Run a complete KFP workflow without K8s-specific knowledge
– How Arrikto’s Rok fits into your plans for Kubeflow

MiniKF: Kubeflow on your laptop

June 14, 2019

In this webinar we will introduce you to MiniKF, the fastest and easiest way to get started with Kubeflow. We will install MiniKF from scratch on a laptop, and show you around the various Kubeflow components and Rok, Arrikto’s data management product.

The latest MinKF delivers a simplified model building experience for data scientists and dramatically improves the workflow to create and run Kubeflow Pipelines. It comes with all the features of Kubeflow v0.5, plus the ability to run end-to-end Kubeflow Pipelines locally starting from your Notebook.

Planet-scale ML Workflows with Advanced Data Management on Kubeflow

January 15th, 2019 | CNCF Webinar Series

In this webinar, we’re going to demonstrate a multi-cloud ML workflow with advanced data management on Kubeflow. We will showcase how a Data Scientist can set up their own ML development environment in minutes, start working locally, and seamlessly extend their workflow to a public cloud.

Takeaways:
• Learn about Kubeflow, a complete ML platform on top of Kubernetes
• Learn why data handling is critical to an end-to-end ML workflow
• Learn how you can have all your work, along with your data, packaged, versioned and reproducible across every step of your ML workflow
• Learn how you can run ML workflows that span hybrid and multi-cloud environments