  Today, machine learning (ML) is widely used in industry to provide the core
functionality of production systems. However, it is practically always used in
production systems as part of a larger end-to-end software system that is made
up of several other components in addition to the ML model. Due to production
demand and time constraints, automated software engineering practices are
highly applicable. The increased use of automated ML software engineering
practices in industries such as manufacturing and utilities requires an
automated Quality Assurance (QA) approach as an integral part of ML software.
Here, QA helps reduce risk by offering an objective perspective on the software
task. Although conventional software engineering has automated tools for QA
data analysis for data-driven ML, the use of QA practices for ML in operation
(MLOps) is lacking. This paper examines the QA challenges that arise in
industrial MLOps and conceptualizes modular strategies to deal with data
integrity and Data Quality (DQ). The paper is accompanied by real industrial
use-cases from industrial partners. The paper also presents several challenges
that may serve as a basis for future studies.
