The digital revolution, rapidly decreasing storage cost, and remarkable results achieved by
state of the art machine learning (ML) methods are driving widespread adoption of ML …

Artificial intelligence (AI) and machine learning (ML) techniques have existed for years, but
new hardware trends and advances in model training and inference have radically improved …

Training an effective Machine learning (ML) model is an iterative process that requires effort
in multiple dimensions. Vertically, a single pipeline typically includes an initial ETL (Extract …

With the rise of deep learning (DL), machine learning (ML) has become compute and data
intensive, typically requiring multi-node multi-GPU clusters. As state-of-the-art models grow …

Since the introduction of Apache YARN, which modularly separated resource management
and scheduling from the distributed programming frameworks, a multitude of YARN-native …

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 …

As machine learning is applied more widely, data scientists often struggle to find or create
end-to-end machine learning systems for specific tasks. The proliferation of libraries and …

As Machine Learning (ML) gains adoption across industries and new use cases,
practitioners increasingly realize the challenges around effectively developing and iterating …

Automated ML is an emerging field in Machine Learning that helps developers and new
data scientists with little data science knowledge build Machine Learning models and …

Abstract 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 …