The trustworthiness of artificial intelligence (AI) has been challenged for quite some time.
The AI results will be trustworthy and reliable if trust-related principles are built into the AI …

A Deep Learning (DL) life cycle involves several data transformations, such as performing
data pre-processing, defining datasets to train and test a deep neural network (DNN), and …

We present HYPPO, a novel system to optimize pipelines encountered in exploratory
machine learning. HYPPO exploits alternative computational paths of artifacts from past …

Machine learning (ML) practitioners and organizations are building model repositories of pre-
trained models, referred to as model zoos. These model zoos contain metadata describing …

Supporting iterative and explorative workflows for develo** machine learning (ML)
models, ML experiment management systems (ML EMSs), such as MLflow, are increasingly …

Recent foundation models are capable of handling multiple machine learning (ML) tasks
and multiple data modalities with the unified base model structure and several specialized …

Machine Learning (ML) has revolutionized various domains, offering predictive capabilities
in several areas. However, with the increasing accessibility of ML tools, many practitioners …

Experiment management systems (EMSs), such as MLflow, are increasingly used to
streamline the collection and management of machine learning (ML) artifacts in iterative and …

Context: In machine learning (ML) applications, assets include not only the ML models
themselves, but also the datasets, algorithms, and deployment tools that are essential in the …

Abstract Machine Learning (ML) experiment management tools support ML practitioners and
software engineers when building intelligent software systems. By managing large numbers …