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Manage and Scale Machine Learning Models for IoT Devices

A common data science internet of things (IoT) use case involves training machine learning models on real-time data coming from an army of IoT sensors.  Some use cases demand that each connected device has its own individual model since many basic machine learning algorithms  often outperform a single complex model. We see this in supply chain optimization, predictive maintenance, electric vehicle charging, smart home management, or any number of other use cases. The problem is this: