The Reasoning Show

The Reasoning Show AI moves fast. Thinking clearly matters more.

The Reasoning Show cuts through the hype to explore how the smartest people in enterprise AI actually make decisions — the strategy, the tradeoffs, and the hard lessons no press release mentions.

Every week, hosts Aaron Delp and Brian Gracely sit down with the founders building the tools, investors funding the shift, and operators running AI in the real world. Not hype. Not panic. Just clear-headed conversations with people who have to make actual decisions.

Because the AI revolution isn't just happening. It's being reasoned through.

New shows every Wednesday and Sunday.

Topics: Enterprise AI strategy · LLMs in production · AI leadership · Agentic AI · Digital Sovereignty · Machine Learning · AI startups · Cloud Computing

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The Reasoning Show

Machine Learning with Kubeflow

June 11, 2019 • Aaron Delp & Brian Gracely

0:00 | 34:07

SHOW: 402

DESCRIPTION: Brian talks with David Aronchick (@aronchick, Head of Open Source Machine Learning @Azure) about the history of the KubeFlow project, how it has evolved as a community, and how KubeFlow is making it easier to get started with Machine Learning on Kubernetes.

SHOW SPONSOR LINKS:

Digital Ocean Homepage
Get Started Now and Get a free $50 Credit on Digital Ocean
Datadog Homepage - Modern Monitoring and Analytics
Try Datadog yourself by starting a free, 14-day trial today. Listeners of this podcast will also receive a free Datadog T-shirt
Get 20% off VelocityConf passes using discount code CLOUD

SHOW INTERVIEW LINKS:

KubeFlow Homepage
Tensorflow Homepage
KubeFlow in 2018 - A Year’s Perspective (lots of projects details and slides)
How to adopt cloud-native machine learning with Kubernetes and Kubeflow

SHOW NOTES:

Topic 1 - Welcome to the show. Tell us about your background, especially as you’ve come to be involved in both open source and machine learning or AI.

Topic 2 - You’ve been involved in the KubeFlow project since its creation a couple of years ago. Can you introduce us to the project and how it’s evolved over the last couple of years?

Topic 3 - The stated goal of KubeFlow is to make machine learning workflows simple, repeatable and scalable. Can you walk us through some of the ways that KubeFlow is beginning to achieve these goals?

Topic 4 - For those people that understand Kubernetes, can you explain how KubeFlow interacts with Kubernetes, and maybe a little bit about how KubeFlow gets value from Kubernetes for these ML workloads?

Topic 5 - What are some of the new areas in this space that you’re excited about?

Topic 6 - For people new to this area, what are some of the easier ways for them to get started?

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