))
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:
SHOW INTERVIEW LINKS:
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?
FEEDBACK?
896 episodes
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:
SHOW INTERVIEW LINKS:
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?
FEEDBACK?
896 episodes
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