ML Infra + Apps
SkyML: Infra-less machine learning on any cloud
Ray Summit 2022
In this talk we will introduce SkyML, a project being developed at UC Berkeley's Sky Computing Lab for "infra-less" machine learning (ML) on any cloud. SkyML offers ML practitioners and researchers ease of use, performance, and cost saving. For ease of use, SkyML abstracts away the cloud and infrastructure on which an ML application, such as training or hyperparameter tuning, is run. For performance, SkyML allows the easy provisioning and usage of best-of-breed accelerators across clouds. For cost saving, SkyML offers support for spot instances and automatic cleanup of resources when they become idle. We'll describe the current system and share our experience on its usage with early users.
About Zhanghao
Zhanghao Wu is a second-year PhD student at UC Berkeley, working with Professor Ion Stoica. He is a core member of the SkyML project, in charge of the SkyML core, job management, auto-stopping, and managed spot. His research interests lie in making machine learning practical by building systems for machine learning and improving the efficiency of machine learning algorithms.
About Zongheng
Zongheng Yang is a computer science PhD Candidate in the UC Berkeley RISELab / Sky Computing Lab, advised by Ion Stoica. His research research focuses on learning and optimizing the capabilities of data systems using deep learning ("ML for Systems"). He is currently working on SkyML, a project that distributes machine learning workloads across clouds.
Zhanghao Wu
PhD Student, UC Berkeley
Zongheng Yang
PhD Candidate, UC Berkeley
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