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

• URL: http://arxiv.org/abs/2303.01998v1
• Date: Fri, 3 Mar 2023 15:10:38 GMT
• Title: MLTEing Models: Negotiating, Evaluating, and Documenting Model and System Qualities
• Authors: Katherine R. Maffey, Kyle Dotterrer, Jennifer Niemann, Iain Cruickshank, Grace A. Lewis, Christian K\"astner
• Abstract summary: MLTE is a framework and implementation to evaluate machine learning models and systems. It compiles state-of-the-art evaluation techniques into an organizational process. MLTE tooling supports this process by providing a domain-specific language that teams can use to express model requirements.
• Score: 1.1352560842946413
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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