Enterprise Machine Learning on K8s: Lessons Learned and the Road Ahead
Kubernetes as a platform is being asked to support an ever increasing range of workloads, including machine learning and big data processing. These new workloads introduce challenges both for both end …
| Talk Title | Enterprise Machine Learning on K8s: Lessons Learned and the Road Ahead |
| Speakers | Timothy Chen (Software Engineer, Cloudera), Tristan Zajonc (CTO of Machine Learning, Cloudera) |
| Conference | KubeCon + CloudNativeCon North America |
| Conf Tag | |
| Location | Seattle, WA, USA |
| Date | Dec 9-14, 2018 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
Kubernetes as a platform is being asked to support an ever increasing range of workloads, including machine learning and big data processing. These new workloads introduce challenges both for both end users and cluster administrators. Data scientists want the flexibility to run any workload and library they require, data engineers want to ensure the scalability and reliability of production workloads, and cluster administrators want to maintain governance and control over cluster resources. At Cloudera, we’ve built a machine learning platform on Kubernetes that seeks to balance these competing objectives. In this talk, we will share some of the key design choices we made, lessons learned supporting large enterprise customers, and our vision of the road ahead for machine learning and AI on Kubernetes.
