Consistent Multi-Cloud AI Lifecycle Management with Kubeflow

Debo Dutta and Xinyuan Huang, Cisco


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, Cisco

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, Cisco

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.

@conference {233007,
author = {Debo Dutta and Xinyuan Huang},
title = {Consistent Multi-Cloud {AI} Lifecycle Management with Kubeflow},
year = {2019},
address = {Santa Clara, CA},
publisher = {{USENIX} Association},
month = may,