  Monitoring machine learning systems post deployment is critical to ensure the
reliability of the systems. Particularly importance is the problem of
monitoring the performance of machine learning systems across all the data
subgroups (subpopulations). In practice, this process could be prohibitively
expensive as the number of data subgroups grows exponentially with the number
of input features, and the process of labelling data to evaluate each
subgroup's performance is costly. In this paper, we propose an efficient
framework for monitoring subgroup performance of machine learning systems.
Specifically, we aim to find the data subgroup with the worst performance using
a limited number of labeled data. We mathematically formulate this problem as
an optimization problem with an expensive black-box objective function, and
then suggest to use Bayesian optimization to solve this problem. Our
experimental results on various real-world datasets and machine learning
systems show that our proposed framework can retrieve the worst-performing data
subgroup effectively and efficiently.
