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Oracle Machine Learning for Spark is supported by Oracle R Advanced Analytics for Hadoop and provides massively scalable machine learning algorithms via an R API for Spark and Hadoop environments for data scientists and application developers to build and deploy machine learning models.
OML4Spark R API provides functions for manipulating data stored in a local File System, HDFS, HIVE, Spark DataFrames, Impala, Oracle Database, and other JDBC sources. OML4Spark takes advantage of all the nodes of a Hadoop cluster for scalable, high performance machine learning modeling in Big Data environments. OML4Spark machine learning algorithms use the expressive R formula object optimized for Spark parallel execution.
OML4Spark brings custom Linear Model (LM), Generalized Linear Model (GLM), and MLP Neural Networks algorithms that execute on Spark infrastructure. OML4Spark provides interfaces to Apache SparkML algorithms, but note that OML4Spark algorithms scale and perform better than SparkML. R functions wrap SparkML algorithms within the OML4Spark framework using the R formula specification and Distributed Model Matrix data structure.
Oracle Cloud SQL and OML4Spark can be combined from Oracle Database or Autonomous Database to address large, complex data-driven problems where the source data and patterns to be discovered may lie in big data, relational data, or some combination of the two. OML4Spark provides options for machine learning processing outside the database or as a powerful component of larger, complex machine learning pipelines.
OML4Spark is supported by Oracle R Advanced Analytics for Hadoop, a component of the Big Data Connectors and provides: