Ease. ML: A Lifecycle Management System for Machine Learning
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Author / Producer
Date
2021
Publication Type
Conference Paper
ETH Bibliography
yes
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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.
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published
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Editor
Book title
Proceedings of the Annual Conference on Innovative Data Systems Research (CIDR), 2021
Journal / series
Volume
Pages / Article No.
Publisher
CIDR
Event
11th Annual Conference on Innovative Data Systems Research (CIDR 2021) (virtual)
Edition / version
Methods
Software
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Date collected
Date created
Subject
Organisational unit
03840 - Egger, Peter / Egger, Peter
09588 - Zhang, Ce (ehemalig) / Zhang, Ce (former)
Notes
Conference lecture held on January 15, 2021. Due to the Coronavirus (COVID-19) the conference was conducted virtually.
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)
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)