Machine Learning on OpenShift and Kubernetes SIG: An Invitation
March 26, 2018 | by
Join the OpenShift Machine Learning Conversation on OpenShift Commons
The Machine Learning on OpenShift Special Interest Group is the starting point for joining the Machine Learning on OpenShift community. Hosted by OpenShift Commons, the Machine Learning on OpenShift SIG meets on the 1st Friday of every month at 9:00 AM PT. Next meeting details here.
The Machine Learning on OpenShift Special Interest Group covers best practices for deploying and managing Machine Learning workloads and applications on OpenShift built using (but not limited to) TensorFlow, Apache Spark and other Open Source ML/AI frameworks. We focus on the developer and devops experience of running Machine Learning applications in Kubernetes. We discuss how to define and run Machine Learning frameworks on Kubernetes, how to scale machine learning models and deploy them to production on OpenShift. We discuss and demo relevant tools and projects, and discuss areas of friction that can lead to suggesting improvements or feature requests. Our goal is to ensure that OpenShift is a first-class host for Machine Learning workloads and to broaden our understanding of the frameworks, tools and other requirements that required for data scientists.
Coming to Kubecon/EU? Come meet us in person at the ML at OpenShift Evening Reception
OpenShift Commons is hosting an evening reception to kick off the week in Copenhagen on May 1st at Kubecon/EU. We'll be showcasing a series of lightning talks by members of the Machine Learning on OpenShift Special Interest Group. Guest speakers include Red Hat's Diane Feddema from the Radanlytics.io team, ML on OpenShift SIG Co-Chair David Aronchick of Google, Juypter.io's Carol Willing, OpenShift's Michael Hausenblas and others. More details here: http://openshiftgathering.com/openshiftgathering/copenhagen
Briefing Videos from Recent Machine on OpenShift SIG Meeting:
You've probably heard about the growth of edge computing, but what is edge? And what does it mean- especially for OpenShift admins? By moving workloads to the edge of the network, devices spend less ...