A reproducibility challenge faces machine learning (ML) systems today. The testing, evaluation, verification, and validation (TEVV) of ML systems presents unique challenges that are often absent in traditional software systems. The introduction of randomness to improve training outcomes and the frequent lack of deterministic modes during development and testing often give the impression that models are difficult to test and produce inconsistent results. However, configurations that increase reproducibility are achievable within ML systems, and they should be made available to the engineering and TEVV communities. In this post from the Carnegie Mellon University Software Engineering Institute, we explain why unpredictability is prevalent, how it can be addressed, and the pros and cons of addressing it. We conclude with why, despite the challenges of addressing unpredictability, it is important for our communities to expect predictable and reproducible modes for ML components, especially for TEVV.
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