Monday, May 20 • 5:00pm - 6:00pm
Consistent Multi-Cloud AI Lifecycle Management with Kubeflow

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The journey or the AI/ML lifecycle consists of several steps ranging from accessing the data to training the models and then deploying it. This process is an involved one and is a subject of rapid engineering (especially in open source) and research (e.g. OpML). In this tutorial, we articulate the technical challenges faced during the AI/ML lifecycle management by a variety of persona ranging from the ML scientist to the ML DevOps engineer. We introduce a consistent platform across multiple clouds called Kubeflow, to help solve the challenges faced in multi-cloud AI/ML lifecycle management.


Debo Dutta

Debo is a distinguished engineer at Cisco where he incubates and now leads an AI/ML systems team. His team’s efforts include major contributions to Kubeflow and neural architecture search (autoML).

Xinyuan Huang

Xinyuan Huang is a software engineer at Cisco, where he works on research and development of AI/ML systems. He is an active member in the Kubeflow community and owner of the Kubebench project.

Monday May 20, 2019 5:00pm - 6:00pm
Winchester Room

Attendees (5)