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MLlib: distributed learning algorithms and pipelines in Apache Spark

Contribution grade

Distributed machine-learning software · Human-designed research method or resource

Reference score145,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Integrated distributed implementations of standard learning methods, optimized communication/linear algebra and a pipeline API with Spark's data-processing ecosystem.

Scientific scope

Integrated distributed implementations of standard learning methods, optimized communication/linear algebra and a pipeline API with Spark's data-processing ecosystem. This2015preprint reports a library first developed in2012 and open-sourced in2013; paper year is not first-release year. Sixteen paper authors do not exhaust140-plus community contributors. Standard learning algorithms, Spark itself and prior pipeline designs retain inherited credit; no biological application outcome is added.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. This2015preprint reports a library first developed in2012 and open-sourced in2013; paper year is not first-release year. Sixteen paper authors do not exhaust140-plus community contributors. Standard learning algorithms, Spark itself and prior pipeline designs retain inherited credit; no biological application outcome is added.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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