Machine learning has evolved into an enabling technology for a wide range of highly
successful applications. The potential for this success to continue and accelerate has placed …

Machine Learning (ML) is now used in a range of systems with results that are reported to
exceed, under certain conditions, human performance. Many of these systems, in domains …

Methods for safety assurance suggested by the ISO 26262 automotive functional safety
standard are not sufficient for applications based on machine learning (ML). We provide a …

Machine learning (ML) plays an ever-increasing role in advanced automotive functionality
for driver assistance and autonomous operation; however, its adequacy from the perspective …

Abstract Machine learning (ML) pervades an increasing number of academic disciplines and
industries. Its impact is profound, and several fields have been fundamentally altered by it …

Machine learning (ML) applications become increasingly common in many domains. ML
systems to execute these workloads include numerical computing frameworks and libraries …

As machine learning (ML) increasingly affects people and society, awareness of its potential
unwanted consequences has also grown. To anticipate, prevent, and mitigate undesirable …

Abstract Context Machine Learning (ML) has been at the heart of many innovations over the
past years. However, including it in so-called “safety-critical” systems such as automotive or …

MLflow is a popular open source platform for managing ML development, including
experiment tracking, reproducibility, and deployment. In this paper, we discuss user …

Machine learning systems are both complex and unique. Complex because they consist of
many different components and involve many different stakeholders. Unique because …