Oracle Machine Learning for R (OML4R) is a comprehensive, database-centric environment for end-to-end analytical processes in R, with immediate deployment to production environments.

OML4R is a set of R packages and database features that enable an R user to operate on database-resident data without using SQL and to run R scripts in one or more embedded R engines that run in the database environment.

Using OML4R from your R session, you have easy access to data in a database instance. You can create and use R objects that correspond to database tables and views - referred to as "proxy objects" that enable in-database data exploration and preparation. OML4R has overloaded functions that translate R operations into SQL that runs in the database that benefits from the query. The database consolidates the SQL and can use in the database that benefits from the query optimization, parallel processing, and scalability features of the database. The database returns the results as R objects, which themselves can be proxy objects.

OML4R 2.0 is available in the R interpreter in Oracle Machine Learning Notebooks in your Oracle Autonomous Database instance. For more information, see Get Started with Notebooks for Data Analysis and Data Visualization. In this environment, all the required components are included, including R, required R libraries, and the R interpreter in OML Notebooks.

Embedded R execution provides some of the most significant advantages of using OML4R. Using Embedded R execution, you can store and run R scripts in the database through either an R interface or a SQL interface or both. The Embedded R execution can be run on Autonomous Database using both SQL and REST APIs. You can use the results of R scripts in SQL-enabled tools for structured data, R objects, and images.