Modern radio continuum surveys such as ASKAP's Evolutionary Map of the Universe (EMU) produce catalogues of tens of millions of sources, far exceeding what manually curated pipelines can process. In this talk, I will present two complementary systems addressing this. RG-CAT uses an object-detection machine-learning model as its backbone to detect and classify radio galaxies — including complex, extended morphologies — directly from survey imaging, feeding into cataloguing scripts that produce science-ready outputs. EMUSE (the Evolutionary Map of the Universe Search Engine) uses a multimodal model to generate and search through image embeddings, making survey catalogues discoverable by the wider research community. These are deployed as containerised, cloud-native services, illustrating how machine learning can serve as the backbone of scalable, end-to-end research pipelines.