Oracle Machine Learning

Oracle Machine Learning accelerates the creation and deployment of machine learning models for data scientists by eliminating the need to move data to dedicated machine learning systems.


Currently, certain OML products are available on specific Oracle Database platforms. Choose the Oracle Database platform you want to use with OML.

OML Notebooks

Data scientists and developers develop analytical solutions through an easy-to-use, multiuser collaborative interface based on Apache Zeppelin notebook technology, supporting interpreters for Python, SQL, and PL/SQL on Oracle Autonomous Database.


Available on: Oracle Autonomous Database Cloud Service

OML AutoML User Interface

A no-code user interface supporting AutoML on Autonomous Database to improve both data scientist productivity and non-expert user access to powerful in-database algorithms for classification and regression.


Available on: Oracle Autonomous Database Cloud Service

OML for SQL

SQL and PL/SQL users leverage in-database computation for data exploration and preparation, machine learning model building, evaluation, and deployment. Leverage scalable in-database machine learning algorithms and make predictions directly in SQL queries.


Available on: Oracle Database (on premises) and Oracle Autonomous Database Cloud Service

OML for Python

Python users gain the performance and scalability of Oracle Autonomous Database for data exploration, preparation, and machine learning from a well-integrated Python interface with support for AutoML and immediate deployment of user-defined Python functions from REST endpoints.


Available on: Oracle Autonomous Database Cloud Service

OML for R

R users gain the performance and scalability of Oracle Database for data exploration, preparation, and machine learning from a well-integrated R interface with support for immediate deployment of user-defined R functions from SQL.


Available on: Oracle Database (on premises) and Oracle Database Cloud Service

OML Services

Reduce time to deploy and manage native in-database models and ONNX-format classification and regression models outside Oracle Autonomous Database. Application developers have easy-to-integrate REST endpoints. Data scientists gain integrated model deployment from the Oracle Machine Learning AutoML User Interface.


Available on: Oracle Autonomous Database Cloud Service

Oracle Data Miner

An extension to Oracle SQL Developer that enables data scientists and citizen data scientists to explore and prepare data, easily build and compare multiple machine learning models, make predictions, and accelerate model deployment.


Available on: Oracle Database (on premises)

OML for Spark

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.


Available on: Oracle Big Data Service

OML Notebooks

Data scientists and developers develop analytical solutions through an easy-to-use, multiuser collaborative interface based on Apache Zeppelin notebook technology, supporting interpreters for Python, SQL, and PL/SQL on Oracle Autonomous Database.

OML AutoML User Interface

A no-code user interface supporting AutoML on Autonomous Database to improve both data scientist productivity and non-expert user access to powerful in-database algorithms for classification and regression.

OML for SQL

SQL and PL/SQL users leverage in-database computation for data exploration and preparation, machine learning model building, evaluation, and deployment. Leverage scalable in-database machine learning algorithms and make predictions directly in SQL queries.

OML for Python

Python users gain the performance and scalability of Oracle Autonomous Database for data exploration, preparation, and machine learning from a well-integrated Python interface with support for AutoML and immediate deployment of user-defined Python functions from REST endpoints.

OML Services

Reduce time to deploy and manage native in-database models and ONNX-format classification and regression models outside Oracle Autonomous Database. Application developers have easy-to-integrate REST endpoints. Data scientists gain integrated model deployment from the Oracle Machine Learning AutoML User Interface.

OML for R

R users gain the performance and scalability of Oracle Database for data exploration, preparation, and machine learning from a well-integrated R interface with support for immediate deployment of user-defined R functions from SQL.

OML for SQL

SQL and PL/SQL users leverage in-database computation for data exploration and preparation, machine learning model building, evaluation, and deployment. Leverage scalable in-database machine learning algorithms and make predictions directly in SQL queries.

Oracle Data Miner

An extension to Oracle SQL Developer that enables data scientists and citizen data scientists to explore and prepare data, easily build and compare multiple machine learning models, make predictions, and accelerate model deployment.

OML for R

R users gain the performance and scalability of Oracle Database for data exploration, preparation, and machine learning from a well-integrated R interface with support for immediate deployment of user-defined R functions from SQL.

OML for Spark

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.