  Machine learning techniques are effective for building predictive models
because they identify patterns in large datasets. Development of a model for
complex real-life problems often stop at the point of publication, proof of
concept or when made accessible through some mode of deployment. However, a
model in the medical domain risks becoming obsolete as patient demographics,
systems and clinical practices change. The maintenance and monitoring of
predictive model performance post-publication is crucial to enable their safe
and effective long-term use. We will assess the infrastructure required to
monitor the outputs of a machine learning algorithm, and present two scenarios
with examples of monitoring and updates of models, firstly on a breast cancer
prognosis model trained on public longitudinal data, and secondly on a
neurodegenerative stratification algorithm that is currently being developed
and tested in clinic.
