How can one build a distributed framework that allows efficient deployment of a wide
spectrum of modern advanced machine learning (ML) programs for industrial-scale …

The rise of big data has led to new demands for machine learning (ML) systems to learn
complex models, with millions to billions of parameters, that promise adequate capacity to …

Machine learning (ML) and statistical techniques are key to transforming big data into
actionable knowledge. In spite of the modern primacy of data, the complexity of existing ML …

Distributed machine learning has typically been approached from a data parallel
perspective, where big data are partitioned to multiple workers and an algorithm is executed …

Applying popular machine learning algorithms to large amounts of data raised new
challenges for the ML practitioners. Traditional ML libraries does not support well processing …

ThispaperpresentsBigDL (adistributeddeeplearning framework for Apache Spark), which
has been used by a variety of users in the industry for building deep learning applications on …

Abstract As Machine Learning (ML) applications embrace greater data size and model
complexity, practitioners turn to distributed clusters to satisfy the increased computational …

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 …

We are at the beginning of the multicore era. Computers will have increasingly many cores
(processors), but there is still no good programming framework for these architectures, and …

Abstract With Cloud Computing emerging as a promising new approach for ad-hoc parallel
data processing, major companies have started to integrate frameworks for parallel data …