Improving Machine Learning Test and Evaluation with MLTE

March 4, 2025 • Podcast

By
Alex Derr, Sebastián Echeverría, Katherine R. Maffey (AI Integration Center, U.S. Army), and Grace Lewis

Machine learning (ML) models commonly experience issues when integrated into production systems. MLTE provides a process and infrastructure for ML test and evaluation.

Publisher

Software Engineering Institute

DOI (Digital Object Identifier)

10.58012/qe6v-vy42

Topic or Tag

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Abstract

Machine learning (ML) models commonly experience issues when integrated into production systems. In this podcast, researchers from the SEI and the U.S. Army AI Integration Center (AI2C) discuss Machine Learning Test and Evaluation (MLTE), a new tool that provides a process and infrastructure for ML test and evaluation. MLTE can aid organizations across the DoD in more effectively negotiating, documenting, and evaluating model and system qualities.

About the Speaker

Alex Derr

Alex Derr graduated Summa Cum Laude from Dakota State University where he earned a BS in computer science and mathematics followed by an MS in computer science.

Sebastián Echeverría

Katherine R. Maffey (AI Integration Center, U.S. Army)

Grace Lewis

Dr. Grace Lewis is a principal researcher at the Carnegie Mellon Software Engineering Institute (SEI), where she conducts applied research on how software engineering and software architecture principles, practices, and tools need to evolve in the face of emerging technologies. She is the principal investigator for the Establishing the Practice …

Software Engineering Institute