Deep learning (DL) has demonstrated its remarkable success in a wide variety of fields. The
development of a DL model is a time-consuming and resource-intensive procedure. Hence …

Prediction queries are widely used across industries to perform advanced analytics and
draw insights from data. They include a data processing part (eg, for joining, filtering …

The broadening adoption of machine learning in the enterprise is increasing the pressure for
strict governance and cost-effective performance, in particular for the common and …

Machine Learning (ML) adoption in the enterprise requires simpler and more efficient
software infrastructure—the bespoke solutions typical in large web companies are simply …

Deep learning model repositories are indispensable in machine learning ecosystems today
to facilitate model reuse. However, existing model repositories provide a bare-bone interface …

Feature stores (also sometimes referred to as embedding stores) are becoming ubiquitous
in model serving systems: downstream applications query these stores for auxiliary inputs at …

Machine learning (ML) training data is often scattered across disparate collections of
datasets, called data silos. This fragmentation poses a major challenge for data-intensive …

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 …

Deep learning enables numerous applications across diverse areas. Data systems
researchers are also increasingly experimenting with deep learning to enhance data …

Deep Neural Networks (DNNs) are witnessing increased adoption in multiple domains
owing to their high accuracy in solving real-world problems. However, this high accuracy …