How to Build Flexible, Portable ML Stacks with Kubeflow and Elastifile (Cloud Next '18)

Опубликовано 26 июля 2018, 21:37

Building any production-ready machine learning system involves various components, often mixing vendors, and hand-rolled solutions. Connecting and managing these services for even moderately sophisticated setups introduces huge barriers of complexity, with data management often emerging as an especially daunting concern. In this session, we will demonstrate how Kubeflow’s support portable ML pipelines integrates with Elastifile’s scalable, high-performance file services to address these challenges both on-premises and in Google Cloud. Join us and learn how to make ML on Kubernetes easy, fast, and extensible.

MLAI235

Event schedule → g.co/next18

Watch more Machine Learning & AI sessions here → bit.ly/2zGKfcg
Next ‘18 All Sessions playlist → bit.ly/Allsessions

Subscribe to the Google Cloud channel! → bit.ly/NextSub


re_ty: Publish; product: Cloud - Containers - Google Kubernetes Engine (GKE); fullname: David Aronchick; event: Google Cloud Next 2018;