Oracle Machine Learning
Oracle Machine Learning enables you to solve key enterprise business problems and accelerates the development and deployment of data science and machine learning-based solutions. Benefit from scalable, automated, and secure machine learning to meet the challenges of data exploration and preparation as well as model building, evaluation, and deployment. Whether your interests include APIs for SQL, Python, R, or REST, or you prefer no-code user interfaces, Oracle provides support for solution development and deployment.
Get Started with Oracle Machine Learning
Deep Dive into Oracle Machine Learning
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 Python, R, SQL, PL/SQL, and markdown interpreters on Oracle Autonomous Database.
Available on: Oracle Autonomous Database
Available 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.
Available on: Oracle Autonomous Database
Available on: Oracle Autonomous Database
OML Monitoring
OML Monitoring provides two powerful features to monitor data and machine learning models.
Available on: Oracle Autonomous Database
- Data Monitoring detects data drift over time and its potential impact on the performance of Machine Learning models. This feature is available in OML UI and OML Services APIs.
- Model Monitoring enables tracking of model performance over time. This feature is currently available in OML Services APIs.
Available on: Oracle Autonomous Database
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 Database Cloud Service) and Oracle Autonomous Database
Available on: Oracle Database (on premises and Database Cloud Service) and Oracle Autonomous Database
OML for Python
Python users gain the performance and scalability of Oracle Database and Oracle Autonomous Database for data exploration, data preparation, and machine learning from a well-integrated Python interface with immediate deployment of user-defined Python functions and API support for automated machine learning (AutoML).
Available on: Oracle Database and Oracle Database Cloud Service 19c and 21c, and Oracle Autonomous Database
Available on: Oracle Database and Oracle Database Cloud Service 19c and 21c, and Oracle Autonomous Database
OML for R
R users gain the performance and scalability of Oracle Database and Oracle Autonomous Database for data exploration, data preparation, and machine learning from a well-integrated R interface with support for immediate deployment of user-defined R functions.
Available on: Oracle Database and Oracle Database Cloud Service, and Oracle Autonomous Database
Available on: Oracle Database and Oracle Database Cloud Service, and Oracle Autonomous Database
OML Services
Reduce time to deploy and manage native in-database models and ONNX-format classification, regression, and clustering models outside for real-time applications using easy-to-integrate REST endpoints. Benefit from integrated model deployment in a few clicks from the Oracle Machine Learning AutoML User Interface.
Available on: Oracle Autonomous Database
Available on: Oracle Autonomous Database
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 and Oracle Autonomous Database
Available on: Oracle Database and Oracle Autonomous Database
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
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 Python, R, SQL, PL/SQL, and markdown interpreters 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 Monitoring
OML Monitoring provides two powerful features to monitor data and machine learning models.
- Data Monitoring detects data drift over time and its potential impact on the performance of Machine Learning models. This feature is available in OML UI and OML Services APIs.
- Model Monitoring enables tracking of model performance over time. This feature is currently available in OML Services APIs.
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, data preparation, and machine learning from a well-integrated Python interface with immediate deployment of user-defined Python functions using SQL and REST APIs as well as API support for automated machine learning (AutoML).
OML for R
R users gain the performance and scalability of Oracle Autonomous Database for data exploration, data preparation, and machine learning from a well-integrated R interface with support for immediate deployment of user-defined R functions using SQL and REST APIs.
OML Services
Reduce time to deploy and manage native in-database models and ONNX-format classification, regression, and clustering models outside for real-time applications using easy-to-integrate REST endpoints. Benefit from integrated model deployment in a few clicks from the Oracle Machine Learning AutoML User Interface.
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 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 Database for data exploration, data preparation, and machine learning from a well-integrated Python interface with immediate deployment of user-defined Python functions from a SQL API and API support for automated machine learning (AutoML).
OML for R
R users gain the performance and scalability of Oracle Database for data exploration, data preparation, and machine learning from a well-integrated R interface with support for immediate deployment of user-defined R functions from a SQL API.
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 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 Database for data exploration, data preparation, and machine learning from a well-integrated Python interface with immediate deployment of user-defined Python functions from a SQL API and API support for automated machine learning (AutoML).
OML for R
R users gain the performance and scalability of Oracle Database for data exploration, data preparation, and machine learning from a well-integrated R interface with support for immediate deployment of user-defined R functions from a SQL API.
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 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.