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arXiv · 2303.01998

MLTEing Models: Negotiating, Evaluating, and Documenting Model and System Qualities

Katherine R. Maffey·Kyle Dotterrer·Jennifer Niemann·Iain Cruickshank·Grace A. Lewis·Christian Kästner
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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.

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Katherine R. Maffey, Kyle Dotterrer, Jennifer Niemann, Iain Cruickshank, Grace A. Lewis, Christian Kästner. 2023-03-03. MLTEing Models: Negotiating, Evaluating, and Documenting Model and System Qualities. https://arxiv.org/abs/2303.01998

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