Automated Labeling Infrastructure for Failure Analysis

The development of intelligent assistants helping Failure Analysis (FA) engineers in their daily work is essential to any digitalization strategy. In particular, these systems must solve various computer vision or natural language processing problems to select the most critical information from heterogeneous data, like images or texts, and present it to the users. Modern artificial intelligence (AI) techniques approach these tasks with machine learning (ML) methods. The latter, however, require large volumes of training data to create models to solve the required problems. In most cases, enterprise clouds store vast volumes of data captured while applying various FA methods. Nevertheless, this data is useless for ML training algorithms since it is stored in forms that can only be interpreted by highly-trained specialists. In this paper, we present an approach to embedding an annotation process in the everyday routines of FA engineers. Its services can easily be embedded in existing software solutions to (i) capture and store the semantics of each data piece in machine-readable form, as well as (ii) provide predictions of ML models trained on previously annotated data to simplify the annotation task. Preliminary experiments of the built prototype show that the extension of an image editor used by FA engineers with the services provided by the infrastructure can significantly simplify and speed up the annotation process.