In recent years, machine learning has transitioned from a field of academic research interest
to a field capable of solving real-world business problems. However, the deployment of …

With an ever-increasing amount of options, the task of selecting machine learning tools for
big data can be difficult. The available tools have advantages and drawbacks, and many …

AI models are increasingly applied in high-stakes domains like health and conservation.
Data quality carries an elevated significance in high-stakes AI due to its heightened …

As a new term in the financial industry, FinTech has become a popular term that describes
novel technologies adopted by the financial service institutions. This term covers a large …

The introduction of machine learning (ML) components in software projects has created the
need for software engineers to collaborate with data scientists and other specialists. While …

As the use of machine learning (ML) models in product development and data-driven
decision-making processes became pervasive in many domains, people's focus on building …

Computer clusters with the shared-nothing architecture are the major computing platforms
for big data processing and analysis. In cluster computing, data partitioning and sampling …

Computational notebooks combine code, visualizations, and text in a single document.
Researchers, data analysts, and even journalists are rapidly adopting this new medium. We …

Adding an ability for a system to learn inherently adds uncertainty into the system. Given the
rising popularity of incorporating machine learning into systems, we wondered how the …

The considerable previous work characterizing visualization usage has focused on low-level
tasks or interactions and high-level tasks, leaving a gap between them that is not addressed …