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- W4323323404 abstract "Many organizations seek to ensure that machine learning (ML) and artificial intelligence (AI) systems work as intended in production but currently do not have a cohesive methodology in place to do so. To fill this gap, we propose MLTE (Machine Learning Test and Evaluation, colloquially referred to as melt), a framework and implementation to evaluate ML models and systems. The framework compiles state-of-the-art evaluation techniques into an organizational process for interdisciplinary teams, including model developers, software engineers, system owners, and other stakeholders. MLTE tooling supports this process by providing a domain-specific language that teams can use to express model requirements, an infrastructure to define, generate, and collect ML evaluation metrics, and the means to communicate results." @default.
- W4323323404 created "2023-03-07" @default.
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- W4323323404 date "2023-03-03" @default.
- W4323323404 modified "2023-09-26" @default.
- W4323323404 title "MLTEing Models: Negotiating, Evaluating, and Documenting Model and System Qualities" @default.
- W4323323404 doi "https://doi.org/10.48550/arxiv.2303.01998" @default.
- W4323323404 hasPublicationYear "2023" @default.
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