  For an AI solution to evolve from a trained machine learning model into a
production-ready AI system, many more things need to be considered than just
the performance of the machine learning model. A production-ready AI system
needs to be trustworthy, i.e. of high quality. But how to determine this in
practice? For traditional software, ISO25000 and its predecessors have since
long time been used to define and measure quality characteristics. Recently,
quality models for AI systems, based on ISO25000, have been introduced. This
paper applies one such quality model to a real-life case study: a deep learning
platform for monitoring wildflowers. The paper presents three realistic
scenarios sketching what it means to respectively use, extend and incrementally
improve the deep learning platform for wildflower identification and counting.
Next, it is shown how the quality model can be used as a structured dictionary
to define quality requirements for data, model and software. Future work
remains to extend the quality model with metrics, tools and best practices to
aid AI engineering practitioners in implementing trustworthy AI systems.
