Automotive Linux Summit & Open Source Summit Japan 2018

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 mixing 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 manually tweaking deployments.

To address these concerns, Google recently launched Kubeflow, a new open source project to make using ML stacks on Kubernetes easy, fast and extensible. Learn in this sessions, how to build ML stake on Kubernetes using Kubeflow.