systemml architecture

Apache SystemML is an optimized big data machine learning platform developed and open sourced by IBM, providing the best workplace for machine learning using big data. It runs on Apache Spark and automatically scales the data, determining line by line whether the code should run on the driver or the Apache Spark cluster. (Recommended learning: phpstorm)

SystemML is declarative machine learning (DML), including linear algebra primitives, statistical functions and ML specified structures, making it easier and more native Expression of ML algorithms.

Algorithms are expressed through R-type or Python-type syntax. DML significantly increases data science productivity by providing flexible expressions of custom analyzes and data independent of the underlying input format and physical data representation.

Secondly, SystemML provides automatic optimization functions to ensure efficiency and scalability through data and cluster features. SystemML can run in MapReduce or Spark environments.

What sets SystemML apart is:

(1) Customizable algorithms

(2) Multiple execution modes, including single, Hadoop batch and Spark batch,

(3) automatic optimization

SystemML advanced machine learning is mainly based on two aspects:

SystemML language, declarative machine learning (DML). SystemML contains linear algebra primitives, statistical functions, and ML-specific structures that make it easier and more native to express ML algorithms. Algorithms are expressed through R-type or Python-type syntax.

DML significantly increases data science productivity by providing flexible expressions of custom analysis and data independent of the underlying input format and physical data representation.

Secondly, SystemML provides automatic optimization functions to ensure efficiency and scalability through data and cluster features. SystemML can run in MapReduce or Spark environments.

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