Efforts to make machine learning more widely accessible have led to a rapid increase in
Auto-ML tools that aim to automate the process of training and deploying machine learning …

Real-world machine learning applications need to be thoroughly evaluated to meet critical
product requirements for model release, to ensure fairness for different groups or …

Many spatial decision support systems suffer from user adoption issues in practice due to
lack of trust, technical expertise, and resources. Automated machine learning has recently …

The incorporation of autonomous computation and artificial intelligence (AI) technologies
into smart agriculture concepts is becoming an expected scientific procedure. The airborne …

AutoML systems can speed up routine data science work and make machine learning
available to those without expertise in statistics and computer science. These systems have …

Machine learning (ML) models can fail in unexpected ways in the real world, but not all
model failures are equal. With finite time and resources, ML practitioners are forced to …

As automated machine learning (AutoML) systems continue to progress in both
sophistication and performance, it becomes important to understand thehow'andwhy'of …

As automated machine learning (AutoML) systems continue to progress in both
sophistication and performance, it becomes important to understand the 'how'and 'why'of …

Automated Machine Learning (AutoML) technology can lower barriers in data work yet still
requires human intervention to be functional. However, the complex and collaborative …

The recent trend of automated machine learning (AutoML) has been driving further
significant technological innovation in the application of artificial intelligence from its …