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This article discusses the current state of machine learning clouds, which are cloud service providers that offer machine learning functionalities. The study evaluates the performance of these platforms on real-world machine learning workloads and compares them to traditional methods. The results show that while machine learning clouds offer a higher level of abstraction, there may be a performance penalty. The study also highlights the evolution of these platforms and the inclusion of automatic machine learning techniques.