AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …

Labeling training data is increasingly the largest bottleneck in deploying machine learning
systems. We present Snorkel, a first-of-its-kind system that enables users to train state-of-the …

Labeling training data is increasingly the largest bottleneck in deploying machine learning
systems. We present Snorkel, a first-of-its-kind system that enables users to train state-of-the …

This is an overview of the end-to-end data cleaning process. Data quality is one of the most
important problems in data management, since dirty data often leads to inaccurate data …

We introduce HoloClean, a framework for holistic data repairing driven by probabilistic
inference. HoloClean unifies existing qualitative data repairing approaches, which rely on …

The first edition of this book appeared in 1991 when the technology was new and there were
not too many products. In the Preface to the first edition, we had quoted Michael Stonebraker …

As deep learning models are applied to increasingly diverse problems, a key bottleneck is
gathering enough high-quality training labels tailored to each task. Users therefore turn to …

Abstract The Internet of Medical Things (IoMT) is the gathering and implementation of
healthcare tools connected to public healthcare technology infrastructure via Internet-based …

Labeling training data is one of the most costly bottlenecks in developing machine learning-
based applications. We present a first-of-its-kind study showing how existing knowledge …

There is now more data to analyze than ever before. As data volume and variety have
increased, so have the ties between machine learning and data integration become …