Kubernetes has quickly become the hybrid solution for deploying complicated workloads anywhere. While it started with just stateless services, customers have begun to move complex workloads to the platform. One of the fastest growing use cases is to use Kubernetes as a platform to run machine learnings apps.
Building any production-ready machine learning system involves various vendors and hand-rolled solutions. Connecting and managing these services for even moderately sophisticated setups introduces huge barriers of complexity in adopting machine learning. Infrastructure engineers will often spend a significant amount of time.
New open source projects like KubeFlow and Fabric for Deep Learning (FfDL) are dedicated to making using ML stacks on Kubernetes easy, fast and extensible. Learn in this sessions, how to build ML stake on Kubernetes with these projects.
Atin Sood is a technical lead at IBM's Watson Studio. For the last 10+ years, Atin has been leading technical teams across IBM focusing on scalable distributed systems and scalable machine learning problems.
Sahdev P. Zala is a senior software engineer and open source developer at IBM. He is a CNCF etcd project maintainer, Kubernetes contributor and co-lead of Kubernetes Provider IBM Cloud. Previously, Sahdev was a core contributor in OpenStack and a Technical Committee member of OASIS... Read More →
Thursday October 11, 2018 5:00pm - 5:30pm EDT
Auditorium A
