In this work, we demonstrate the unique advantages of using event-based imagers for optical in-process monitoring of metallic manufacturing processes that feature a melt pool (e.g., welding, additive manufacturing). In-process monitoring is an important problem that must be solved to allow the use of additive manufacturing for mission-critical metallic components. Unfortunately, there are a number of challenges that make in-process monitoring difficult for the high-temperature, high light intensity environment presented by metallic additive manufacturing. The first problem is that conventional imagers become saturated and typically do not have the dynamic range needed to observe melt pool processes. High dynamic range imagers exist, but they typically only have a framerate on the order of tens of Hertz which is not suitable for the fast dynamics occurring in a melt pool on the order of hundreds of Hertz. High speed laser illumination can be used with high-speed cameras, but these approaches are expensive and result in extremely large amounts of data (order terabytes) not suitable for in-process monitoring or forming digital twins of additively manufactured components. To solve these issues, we propose the use of event-based imagers. Event-based sensing is an alternative measurement paradigm where data is only transmitted/recorded when changes in light intensity exceeds a set threshold. The result is that event-based sensors consume less power and less memory/bandwidth, and they operate across a wide range of timescales and dynamic ranges. In this work we will demonstrate the high dynamic range properties of event-based imagers and their ability to observe melt pools produced by electric arcs and lasers. We demonstrate the efficient nature of the sampling associated with event-driven imagers in the context of observing melt pool dynamics and the resulting memory savings. We demonstrate the ability to detect and track contaminates in the melt pool. Finally we demonstrate unsupervised learning of principle components of melt pools in order to facilitate additional data compression for down-stream processing and control.