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Project Summary

Declarative large-scale machine learning (ML) that aims at flexible specification of ML algorithms and automatic generation of hybrid runtime plans ranging from single-node, in-memory computations, to distributed computations on Apache Hadoop and Apache Spark.
ML algorithms are expressed in an R-like or Python-like syntax that includes linear algebra primitives, statistical functions, and ML-specific constructs. This high-level language significantly increases the productivity of data scientists as it provides (1) full flexibility in expressing custom analytics, and (2) data independence from the underlying input formats and physical data representations. Automatic optimization according to data and cluster characteristics ensures both efficiency and scalability.

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cluster distributed dml hadoop java machine_learning pydml python spark

In a Nutshell, Apache SystemML...

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This Project has No vulnerabilities Reported Against it

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Languages

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Java
86%
16 Other
14%

30 Day Summary

May 18 2018 — Jun 17 2018

12 Month Summary

Jun 17 2017 — Jun 17 2018
  • 992 Commits
    Up + 68 (7%) from previous 12 months
  • 27 Contributors
    Down -7 (20%) from previous 12 months