Abstract
We present Ease.ML, a lifecycle management system for machine learning (ML). Unlike many existing works, which focus on improving individual steps during the lifecycle of ML application development, Ease.ML focuses on managing and automating the entire lifecycle itself. We present user scenarios that have motivated the development of Ease.ML, the eight-step Ease.ML process that covers the lifecycle of ML application development; the foundation of Ease.ML in terms of a probabilistic database model and its connection to information theory; and our lessons learned, which hopefully can inspire future research. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000458916Publication status
publishedBook title
Proceedings of the Annual Conference on Innovative Data Systems Research (CIDR), 2021Publisher
CIDREvent
Organisational unit
09588 - Zhang, Ce / Zhang, Ce03840 - Egger, Peter / Egger, Peter
Funding
184628 - EASEML: Toward a More Accessible and Usable Machine Learning Platform for Non-expert Users (SNF)
187132 - Machine‐based Scoring of a Neuropsychological Test: The Rey‐Osterrieth Complex Figure (SNF)
957407 - Integrated Data Analysis Pipelines for Large-Scale Data Management, HPC, and Machine Learning (EC)
Notes
Conference lecture held on January 15, 2021. Due to the Coronavirus (COVID-19) the conference was conducted virtually.More
Show all metadata
ETH Bibliography
yes
Altmetrics