Apache Spark has emerged as the de facto framework for big data analytics with its
advanced in-memory programming model and upper-level libraries for scalable machine …

Large-scale data analytics using statistical machine learning (ML), popularly called
advanced analytics, underpins many modern data-driven applications. The data …

Today's artificial intelligence still faces two major challenges. One is that, in most industries,
data exists in the form of isolated islands. The other is the strengthening of data privacy and …

The appeal of serverless (FaaS) has triggered a growing interest on how to use it in data-
intensive applications such as ETL, query processing, or machine learning (ML). Several …

The rising need for custom machine learning (ML) algorithms and the growing data sizes
that require the exploitation of distributed, data-parallel frameworks such as MapReduce or …

Machine learning can provide deep insights into data, allowing machines to make high-
quality predictions and having been widely used in real-world applications, such as text …

We investigate the problem of building least squares regression models over training
datasets defined by arbitrary join queries on database tables. Our key observation is that …

We present a platform built on large-scale, data-centric machine learning (ML) approaches,
whose particular focus is demand forecasting in retail. At its core, this platform enables the …

Deep neural networks (deep nets) are revolutionizing many machine learning (ML)
applications. But there is a major bottleneck to wider adoption: the pain and resource …

Slice finding---a recent work on debugging machine learning (ML) models---aims to find the
top-K data slices (eg, conjunctions of predicates such as gender female and degree PhD) …