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Artificial intelligence (AI) has a quality problem. And it’s big. According to Gartner, 54% of machine learning (ML) models never make it into production, primarily because of poor quality. Sometimes, the models don’t work as intended and developers can’t identify the cause to fix it. Other times, the models work but aren’t explainable, so they can’t be deployed for compliance reasons. Sometimes, even when the model’s results are explainable, stakeholders don’t trust them and they aren’t deployed. There are four important ML model testing criteria and developers need all four to ensure their models make it over the finish line.