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Machine learning applications have become ubiquitous in a variety of domains. Powering each of these ML applications are one or more machine learning models that are used to make key decisions or compute key quantities. The life-cycle of an ML model starts with data processing, going on to feature engineering, model experimentation, deployment, and maintenance. We call the process of tracking a model across all phases of its life-cycle as model management. In this paper, we discuss the currentdblp:journals/debu/VartakM18 fatcat:ku5ohu2ty5dlfaueohtmexdnuu