Standard earthquake monitoring workflows can be described with the following sequence of steps: (1) pre-processing, (2) phase detection, (3) event detection/phase association, (4) event location, and (5) event characterization. Neural networks have been shown capable of replacing each of these steps, and in some cases lead to dramatic improvement. In this talk I will present progress on using machine learning on continuous ground motion recorded across a seismic network to generate earthquake catalogs that are far more comprehensive than those developed using standard approaches. A combination of appropriate architecture, accurate data labels, and data augmentation all play an important role in developing effective models. The simplest approach to implement machine-learning-based monitoring is modular – to replace individual earthquake monitoring steps one-by-one with neural network models. There can be advantages, however, in combining steps in multi-task models to take advantage of contextual information. Moreover, it is possible to combine all steps in an end-to-end model, which could hold advantages over the modular approach. In this talk I will demonstrate each of these possibilities and illustrate them with real-world examples.