Over the last couple of decades, those looking for a cluster management platform faced no shortage of choices. However, large-scale clusters are being asked to operate in different ways, namely by ...

More information: Faye Orcales et al, Using genomic data and machine learning to predict antibiotic resistance: A tutorial paper, PLOS Computational Biology (2024). DOI: 10.1371/journal.pcbi.1012579 ...

Uber AI has open-sourced Fiber, a new library which aims to empower users in implementing large-scale machine learning computation on computer clusters. The main objectives of the library are to lever ...

While there are many more machine learning frameworks available than are mentioned in this article, the frameworks mentioned here are well-supported and robust, and will help users to succeed in their ...

Apache Spark is a hugely popular open source platform for data science and machine learning, commercially supported by Databricks. Spark supports in-memory processing and scales well via clustering.

Training a machine learning algorithm to accurately solve complex problems requires large amounts of data. Previous articles in this series discussed an exascale-capable machine learning algorithm and ...

“In addition to applications in the military domain, coresets and distributed machine learning in general are also widely applicable in the commercial setting, where multiple organizations would ...

The company also argues that this allows it to train large models that are bigger than the individual GPU memory capacity of a single machine. There’s a financial aspect to this, too, because ...