Modern data workflows are increasingly burdened by growing dataset sizes and the complexity of distributed processing. Many organizations find that traditional systems struggle with long processing times, memory constraints, and managing distributed tasks effectively. In this environment, data scientists and engineers often spend excessive time on system maintenance rather than extracting insights from data. The… →

Large Language Models (LLMs) are widely used in medicine, facilitating diagnostic decision-making, patient sorting, clinical reporting, and medical research workflows. Though they are exceedingly good in controlled medical testing, such as the United States Medical Licensing Examination (USMLE), their utility for real-world uses is still not well-tested. Most existing evaluations rely on synthetic benchmarks that… →

Handling personally identifiable information (PII) in large language models (LLMs) is especially difficult for privacy. Such models are trained on enormous datasets with sensitive data, resulting in memorization risks and accidental disclosure. Managing PII is complex because datasets are constantly updated with new information, and some users may request data removal. In fields like healthcare,… →

Creating charts that accurately reflect complex data remains a nuanced challenge in today’s data visualization landscape. Often, the task involves not only capturing precise layouts, colors, and text placements but also translating these visual details into code that reproduces the intended design. Traditional methods, which rely on direct prompting of vision-language models (VLMs) such as… →