Development of machine learning (ML) workflows is a tedious process of iterative
experimentation: developers repeatedly make changes to workflows until the desired …

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

Abstract Machine learning is a powerful tool for gleaning knowledge from massive amounts
of data. While a great deal of machine learning research has focused on improving the …

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 …

Creating and maintaining a platform for reliably producing and deploying machine learning
models requires careful orchestration of many components---a learner for generating …

Modern companies and institutions rely on data to guide every single business process and
decision. Missing or incorrect information seriously compromises any decision process …

Machine learning has become an essential tool for gleaning knowledge from data and
tackling a diverse set of computationally hard tasks. However, the accuracy of a machine …

Increasingly larger number of software systems today are including data science
components for descriptive, predictive, and prescriptive analytics. The collection of data …

The training, maintenance, deployment, monitoring, organization and documentation of
machine learning (ML) models–in short model management–is a critical task in virtually all …

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 …