Data-centric AI is at the center of a fundamental shift in software engineering where machine
learning becomes the new software, powered by big data and computing infrastructure …

Companies struggle to continuously develop and deploy Artificial Intelligence (AI) models to
complex production systems due to AI characteristics while assuring quality. To ease the …

Organizations rely on machine learning engineers (MLEs) to operationalize ML, ie, deploy
and maintain ML pipelines in production. The process of operationalizing ML, or MLOps …

Artificial intelligence (AI) systems are trained to solve complex problems and learn to
perform specific tasks by using large volumes of data, such as prediction, classification …

Developing machine learning models can be seen as a process similar to the one
established for traditional software development. A key difference between the two lies in the …

The explorative and iterative nature of developing and operating ML applications leads to a
variety of artifacts, such as datasets, features, models, hyperparameters, metrics, software …

When employing supervised machine learning to analyze network traffic, the heart of the
task often lies in developing effective features for the ML to leverage. We develop GGFAST …

Purpose Recent advancements in Artificial Intelligence (AI) and, at its core, Machine
Learning (ML) offer opportunities for organizations to develop new or enhance existing …

The ever-growing demand and complexity of machine learning are putting pressure on
hyper-parameter tuning systems: while the evaluation cost of models continues to increase …

Black-box optimization (BBO) has a broad range of applications, including automatic
machine learning, experimental design, and database knob tuning. However, users still face …