  As Deep Neural Networks (DNNs) have become an increasingly ubiquitous
workload, the range of libraries and tooling available to aid in their
development and deployment has grown significantly. Scalable, production
quality tools are freely available under permissive licenses, and are
accessible enough to enable even small teams to be very productive. However
within the research community, awareness and usage of said tools is not
necessarily widespread, and researchers may be missing out on potential
productivity gains from exploiting the latest tools and workflows. This paper
presents a case study where we discuss our recent experience producing an
end-to-end artificial intelligence application for industrial defect detection.
We detail the high level deep learning libraries, containerized workflows,
continuous integration/deployment pipelines, and open source code templates we
leveraged to produce a competitive result, matching the performance of other
ranked solutions to our three target datasets. We highlight the value that
exploiting such systems can bring, even for research, and detail our solution
and present our best results in terms of accuracy and inference time on a
server class GPU, as well as inference times on a server class CPU, and a
Raspberry Pi 4.
