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.
In a Nutshell, Apache SystemML...
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has a well established, mature codebase
maintained by a very large development team
with decreasing Y-O-Y commits -
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took an estimated 602 years of effort (COCOMO model)
starting with its first commit in January, 2012
ending with its most recent commit 2 days ago
Quick Reference
Apache License 2.0
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These details are provided for information only. No information here is legal advice and should not be used as such.
This Project has No vulnerabilities Reported Against it
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