+The increasing application of machine learning (ML) models in modern industrial settings raises more and more challenges along the lifecycle of a model: from training to deployment, execution, and adaptation. Due to the nature of fully automatized, just-in-time production lines, machine learning models not only need to be accurate, but also quick-to-serve predictions. Additionally, the long service life of industrial plants and associated high costs require dependable, trustworthy models that are easy to maintain. Changing parameters, e.g., production schedules, machine tooling, or material degradation, demand continuous monitoring of model performance and respective adaption. Such issues need to be addressed by the software components of a comprehensive industrial machine learning solution. In this track, we invite researchers to present software design aspects of new solutions to these challenges and lessons learned from real-world applications to avoid common pitfalls. For contributions to this track, topics of interest include, but are not limited to:
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