Real-time gravitational wave astronomy stands to benefit substantially from the adoption of machine learning algorithms, which have demonstrated an ability to model complex signals, even in the presence of considerable noise, with minimal run-time latency and compute requirements. Moreover, many gravitational wave event morphologies and noise sources are well understood and easily simulated, acting as physical priors which can be exploited to regularize training to produce more robust models. However, adoption of production ML systems in this setting has been impeded by a lack of software tools simplifying the development of experimental and deployment pipelines that leverage these priors in a computationally efficient manner. In this demo, we’ll introduce ml4gw and hermes, two libraries for accelerating training and inference of models in the context of gravitational waves, and show how they can be combined with other infrastructure tools to build, evaluate, and deploy a competitive model for detecting binary black hole mergers in real LIGO gravitational strain data.