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Spark is a cluster computing framework, and MLlib is a distributed machine learning library on top of Spark.
The models were trained using the machine learning library LIBSVM.
Another advantage of Spark is the scalable machine learning library, MLlib.
All methods and evaluations were implemented in an in-house Java-based machine learning library.
Flink-ML is a machine learning library currently in development for the Flink platform.
LibSVM Version 3.18 [21] for a machine learning library is available.
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We will also briefly discuss machine learning libraries such as Sklearn.
The second segment is represented by device-centric AI supported by mobile machine learning libraries like Apple's CoreML.
The platform comes with native tools for DataFrame manipulation, GPU-accelerated machine learning libraries, graph analytics libraries and deep learning frameworks.
In addition to these wrappers, some researchers have also developed scalable machine learning libraries such as Mahout [14] using MapReduce paradigm.
RAPIDS, a set of open source machine learning libraries, focuses on common data preparation tasks for analytics and data science, enabling end-to-end, GPU-accelerated pipelines.
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