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As Multi-access Edge Computing (MEC) and 5G technologies evolve, new applications are emerging with unprecedented capacity and real-time requirements. At the core of such applications there is a need for machine learning (ML) to create value from the data at the edge. Current ML systems transfer data from geo-distributed streams to a central datacenter for modeling. The model is then moved to the edge and used for inference or classification. These systems can be ineffective because they introduce significant demand for data movement and model transfer in the critical path of learning. Furthermore, a full model may not be needed at each edge location. An alternative is to train and update the models online at each edge with local data, in isolation from other edges. Still, this approach can worsen the accuracy of models due to reduced data availability, especially in the presence of local data shifts.