Training machine learning models requires feeding input data for models to ingest. Input
pipelines for machine learning jobs are often challenging to implement efficiently as they …

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 …

Machine learning (ML) is now commonplace, powering data-driven applications in various
organizations. Unlike the traditional perception of ML in research, ML production pipelines …

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

Since its invention, data-centric code generation has been adopted for query compilation by
various database systems in academia and industry. These database systems are fast but …

The huge demand for computation in artificial intelligence (AI) is driving unparalleled
investments in hardware and software systems for AI. This leads to an explosion in the …

Deep learning (DL) is growing in popularity for many data analytics applications, including
among enterprises. Large business-critical datasets in such settings typically reside in …

User-defined functions (UDFs) have been widely used to overcome the expressivity
limitations of SQL and complement its declarative nature with functional capabilities. UDFs …

Today's users of data processing systems come from different domains, have different levels
of expertise, and prefer different programming languages. As a result, analytical workload …

The recent success of machine learning (ML) has led to an explosive growth of systems and
applications built by an ever-growing community of system builders and data science (DS) …