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With the increased interest to incorporate machine learning into software and systems, methods to characterize the impact of the reliability of machine learning are needed to ensure the reliability of the software and systems in which these algorithms reside. Towards this end, we build upon the architecture-based approach to software reliability modeling, which represents application reliability in terms of the component reliabilities and the probabilistic transitions between the components. Traditional architecture-based software reliability models consider all components to be deterministic software. We therefore extend this modeling approach to the case, where some components represent learning enabled components. Here, the reliability of a machine learning component is interpreted as the accuracy of its decisions, which is a common measure of classification algorithms. Moreover, we allow these machine learning components to be fault-tolerant in the sense that multiple diverse classifier algorithms are trained to guide decisions and the majority decision taken. We demonstrate the utility of the approach to assess the impact of machine learning on software reliability as well as illustrate the concept of reliability growth in machine learning. Finally, we validate past analytical results for a fault tolerant system composed of correlated components with real machine learning algorithms and data, demonstrating the analytical expression’s ability to accurately estimate the reliability of the fault tolerant machine learning component and subsequently the architecture-based software within which it resides.