Seattle, WA
December 10–13, 2018
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Tuesday, December 11 • 3:40pm - 4:15pm
Machine Learning as Code: and Kubernetes with Kubeflow - Jason " Jay" Smith, Google & David Aronchick

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Machine Learning is become an increasingly popular topic in the world of data. At the same time, the concept of microservices through containerization has increased in popularity as it allows for developers to create and package applications for easy export and distribution through various clouds.

Kubeflow is an open source project lead by Google to merge both concepts, allowing users to leverage the power of Kubernetes to run the training and serving of their ML models.

This convergence of technologies does result in a new way to think of Machine Learning. We now think of can think of machine learning as code bundles. My session will show how with Kubeflow and GitOps tools, you can go beyond simply deploying and training TensorFlow models but bundling the entire infrastructure into a code package and treat the entire machine learning process as a pipeline.

avatar for David Aronchick

David Aronchick

Head of OSS Machine Learning, Microsoft
David leads Open Source Machine Learning Strategy at Azure. This means he spends most of his time helping humans to convince machines to be smarter. He is only moderately successful at this.Previously, he led product management for Kubernetes, launched Google Kubernetes Engine and... Read More →
avatar for Jay Smith

Jay Smith

Cloud Customer Engineer 云客户工程师, Google
Jason ‘Jay’ Smith is a Cloud Customer Engineer at Google. He is passionate containerization and machine learning so Kubeflow was a natural fit as it allows users to run machine learning models on Kubernetes. He has spent much time learning about Kubernetes and has become interesting... Read More →

Tuesday December 11, 2018 3:40pm - 4:15pm PST