September 24, 2019

145 words 1 min read

Manage Multi-tenant ML Workloads Using Istio

Manage Multi-tenant ML Workloads Using Istio

With rapid growth of machine learning workloads deployed on Kubernetes, it is becoming a popular demand to offer a multi-tenant pipeline to manage machine learning workloads that facilitates different …

Talk Title Manage Multi-tenant ML Workloads Using Istio
Speakers Limin Wang (Staff Software Engineer, Google), Wencheng Lu (Senior Staff Software Engineer, Google)
Conference KubeCon + CloudNativeCon
Conf Tag
Location Shanghai, China
Date Jun 23-26, 2019
URL Talk Page
Slides Talk Slides
Video

With rapid growth of machine learning workloads deployed on Kubernetes, it is becoming a popular demand to offer a multi-tenant pipeline to manage machine learning workloads that facilitates different data scientists to collect data, train and serve models on kubernetes. Come learn how Istio can be integrated into a multi-tenant machine learning pipeline like Kubeflow to provide isolation and protection of workloads deployed for different users through sufficient identity, access, and api management

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