  Artificial Intelligence for IT Operations (AIOps) has been adopted in
organizations in various tasks, including interpreting models to identify
indicators of service failures. To avoid misleading practitioners, AIOps model
interpretations should be consistent (i.e., different AIOps models on the same
task agree with one another on feature importance). However, many AIOps studies
violate established practices in the machine learning community when deriving
interpretations, such as interpreting models with suboptimal performance,
though the impact of such violations on the interpretation consistency has not
been studied. In this paper, we investigate the consistency of AIOps model
interpretation along three dimensions: internal consistency, external
consistency, and time consistency. We conduct a case study on two AIOps tasks:
predicting Google cluster job failures, and Backblaze hard drive failures. We
find that the randomness from learners, hyperparameter tuning, and data
sampling should be controlled to generate consistent interpretations. AIOps
models with AUCs greater than 0.75 yield more consistent interpretation
compared to low-performing models. Finally, AIOps models that are constructed
with the Sliding Window or Full History approaches have the most consistent
interpretation with the trends presented in the entire datasets. Our study
provides valuable guidelines for practitioners to derive consistent AIOps model
interpretation.

Machine Learning;Software Engineering