Running Machine Learning Workloads On Any Cloud

Zongheng Yang on Sky Computing and SkyPilot.


SubscribeApple • Spotify • Stitcher • Google • AntennaPod • Podcast Addict • Amazon •  RSS.

Zongheng Yang, is a researcher in the Sky Computing Lab at UC Berkeley, a multi-year research initiative that utilizes distributed systems, programming languages, security and machine learning to separate the services that a company requires from the choice of a specific cloud. He provides a detailed overview and update on SkyPilot, a groundbreaking intercloud broker that views the cloud ecosystem as a unified and integrated entity rather than a collection of disparate, largely incompatible clouds. SkyPilot enables users to run Machine Learning and Data Science batch jobs on any cloud, realize substantial cost savings, access the best hardware across clouds, and enjoy higher resource availability. Given the difference in pricing and hardware offerings between regions, SkyPilot has also become an important tool for users who rely on a single cloud provider.

Subscribe to the Gradient Flow Newsletter

 

Interview highlights – key sections from the video version:

 

Related content:


If you enjoyed this episode, please support our work by encouraging your friends and colleagues to subscribe to our newsletter:



[Image from Zongheng Yang, used with permission.]