Virtual Event
November 17, 2020
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Tuesday, November 17 • 5:35pm - 6:20pm
Running Machine Learning Workloads on a Service Mesh

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Data security is one of the key pillars to ensure successful operationalization of machine learning workloads. A service mesh can help build capabilities around mTLS, authorization checks combined with some other goodies to add security, resilience and observability to existing services and applications. JupyterHub is one of the most popular open source tools of choice for teams running machine learning environments. There has been a lot of demand in the community to add support for running JupyterHub with a service mesh on Kubernetes. This talk would cover the journey of adding Istio ServiceMesh support to JupyterHub, the roadblocks, the troubleshooting journey and how Istio makes operating and securing machine learning workloads easier despite the heterogeneous nature of tools that the data scientists use. This combined with network policies and other security best practices for running workloads on Kubernetes makes for a great operational and usability combo.

avatar for Harsimran Singh Maan

Harsimran Singh Maan

Engineer, Splunk

Tuesday November 17, 2020 5:35pm - 6:20pm EST