Meeting the latency requirements of 5G networks requires massive parallelization. We'll discuss how to parallelize and map certain radio access network (RAN) functions to GPU architectures to achieve orders-of-magnitude acceleration. We'll describe how to realize selected RAN functions using online machine learning methods. We'll also explore the possibility of a machine learning function orchestrator (MLFO) in the context of end-to-end network slicing where deep neural networks are an interesting option. Our talk will use findings of the ITU-T focus group on machine learning for 5G to explore the challenge of implementing MLFO, leading to new mobile network architectures.