In Precision Livestock Farming (PLF), deep learning-based approaches are increasingly employed to study animal behavior on farms. These behavioral studies enable animal phenotyping, which can be used for genetic selection and social network analysis in large animal groups. While considerable attention is often given to models in the deep learning field, the models themselves do not function in isolation. Efficient deep-learning workflows require systems that bridge research and prototyping with production operations.

We introduce the IMAGEN Data Analytics Platform under the IMAGEN program, which brings research into animal behavior together with computer science to improve the health and welfare of pigs and lay hens and reduce the ecological footprint of food production. This platform on the Surf HPC cluster supports the development of deep learning models for animal phenotyping and addresses various data challenges through a DataOps approach. Although initially designed for the animal phenotype detection domain, the platform is domain-neutral and can be applied to similar cases in other application domains.