Introduction to Kubeflow

In this episode, Michael will introduce us to Kubeflow's new features, shipping in Kubeflow 1.3, and guide us through everything we need to get started running our data science workloads on Kubernetes with Kubeflow.

🍿 Rawkode Live

Hosted by David McKay / 🐦 https://twitter.com/rawkode
Website: https://rawkode.live
Discord Chat: https://rawkode.live/chat

#RawkodeLive

🕰 Timeline

00:00 - Holding screen
00:40 - Introductions
06:00 - What is Kubeflow?
42:00 - Introduction to Machine Learning by Example
59:00 - Working with Kubeflow Notebooks
1:54:00 - Deploying with Kale

👥 About the Guests

Michael Tanenbaum

Solutions Engineer @arrikto

🐦 https://twitter.com/tbaums
🧩 https://github.com/tbaums

🔨 About the Technologies

Kubeflow

The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures. Anywhere you are running Kubernetes, you should be able to run Kubeflow.

🌏 https://www.kubeflow.org/
🐦 https://twitter.com/kubeflow
🧩 https://github.com/kubeflow/kubeflow

#Deep Learning #Machine Learning #Data Science

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