Machine Learning in Oracle Database
Machine Learning in Oracle Database supports data exploration, preparation, and machine learning modeling at scale using SQL, R, Python, REST, AutoML, and no-code interfaces. It includes more than 30 high-performance in-database algorithms producing models for immediate use in applications. By keeping data in the database, organizations can simplify their overall architecture and maintain data synchronization and security. It enables data scientists and other data professionals to build models quickly by simplifying and automating key elements of the machine learning lifecycle.
See how to build machine learning models faster with Python, R and SQL.
Build high performance machine learning models in Oracle Database
Machine Learning in Autonomous Database
Oracle Machine Learning Notebooks
Increase data scientist and developer productivity and reduce their learning curve with familiar open source-based Apache Zeppelin notebook technology. Notebooks support SQL, PL/SQL, Python, and markdown interpreters for Oracle Autonomous Database so users can work with their language of choice when developing analytical solutions.
- View interactive product tour
- Oracle Machine Learning Notebooks with Autonomous Data Warehouse documentation
- Oracle Machine Learning Notebooks with Autonomous Transaction Processing documentation
- Oracle Machine Learning Notebooks with Autonomous JSON Database documentation
Oracle Machine Learning Services
Reduce time to deploy and manage native in-database models and ONNX-format models in the Oracle Autonomous Database environment. Application developers use models through easy-to-integrate REST endpoints. Deploy models quickly and easily from the Oracle Machine Learning AutoML User Interface.
- REST API for Oracle Machine Learning Services
- Work with Cognitive Text Functionality
- GitHub repository
Oracle Machine Learning for SQL
Simplify and accelerate the creation of machine learning models for both expert and non-expert data scientists alike, using familiar SQL and PL/SQL for data preparation, model building, evaluation, and deployment.
Oracle Machine Learning AutoML User Interface
A no-code user interface supporting AutoML on Oracle Autonomous Database to improve both data scientist productivity and non-expert user access to powerful in-database algorithms for classification and regression.
Oracle Machine Learning for Python
Data scientists and other Python users accelerate machine learning modeling and solution deployment by using Oracle Autonomous Database as a high-performance computing platform with a Python interface. Built-in automated machine learning (AutoML) recommends relevant algorithms and features for each model, and performs automated model tuning. Together, these capabilities enhance user productivity, model accuracy, and scalability.
Oracle Data Miner
Data scientists and data analysts can use this drag-and-drop user interface to quickly build analytical workflows. Rapid model development and refinement allows users to discover hidden patterns, relationships, and insights in their data.
Machine learning in Oracle Database and cloud database services
Oracle Machine Learning for SQL
Simplify and accelerate the creation of machine learning models for data scientists and citizen data scientists, using familiar SQL and PL/SQL for data preparation, model building, evaluation, and deployment.
Oracle Data Miner
Data scientists and data analysts can use this drag-and-drop user interface to quickly build analytical workflows. Rapid model development and refinement allows users to discover hidden patterns, relationships, and insights in their data.
Oracle Machine Learning for R
Accelerate machine learning modeling and solution deployment by using Oracle Database as a high-performance computing platform with an R interface. Easily deploy user-defined R functions from SQL and R APIs with data-parallel and task-parallel options. User-defined R functions can include functionality from the R package ecosystem.
Oracle Machine Learning for Python
Data scientists and other Python users accelerate machine learning modeling and solution deployment by using Oracle Autonomous Database as a high-performance computing platform with a Python interface. Built-in automated machine learning (AutoML) recommends relevant algorithms and features for each model, and performs automated model tuning. Together, these capabilities enhance user productivity, model accuracy, and scalability.
AutoML in Oracle Database
Oracle Machine Learning AutoML User Interface
A no-code user interface supporting AutoML on Oracle Autonomous Database to improve both data scientist productivity and non-expert user access to powerful in-database algorithms for classification and regression.
Oracle Machine Learning for Python
Data scientists and other Python users accelerate machine learning modeling and solution deployment by using Oracle Autonomous Database as a high-performance computing platform with a Python interface. Built-in automated machine learning (AutoML) recommends relevant algorithms and features for each model, and performs automated model tuning. Together, these capabilities enhance user productivity, model accuracy, and scalability.
No-code User Interfaces for Machine Learning in Oracle Database
Oracle Machine Learning AutoML user interface
A no-code user interface supporting AutoML on Oracle Autonomous Database to improve both data scientist productivity and non-expert user access to powerful in-database algorithms for classification and regression.
Oracle Data Miner
Data scientists and data analysts can use this drag-and-drop user interface to quickly build analytical workflows. Rapid development and refinement allows users to discover hidden patterns, relationships, and insights in their data.
See what top industry analysts are saying about us
Dragon Slayer Consulting: Oracle Autonomous Database disrupts database IT outsourcing
Read the Dragon Slayer Consulting report (PDF)
Enterprise Strategy Group finds Oracle’s Autonomous Data Warehouse enhancements “democratize simplicity”
Read the Enterprise Strategy Group blog
OMDIA: Oracle is the only vendor that lets customers choose which cloud services to run on-premises and in public cloud
Read the OMDIA report (PDF)Machine Learning in Oracle Database customer successes
Customers around the world take advantage of Oracle’s in-database machine learning capabilities to solve complex and important data-driven problems.
UK NHS saves £1.6 billion and provides better personalized care with Machine Learning in Oracle Database.
Benefits
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In-database model building and data scoring
Data scientists and developers build models and score data faster and at scale with no need to extract data to separate analytics engines. Oracle Exadata’s scale-out architecture and Smart Scan technology delivers fast results.
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Security
Data scientists and developers using Machine Learning in Oracle Database are protected with built-in security, encryption, and role-based access to user data and models.
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Rapid enterprise deployments
Developers and the broader data science team achieve immediate machine learning model availability with easy deployment options using SQL and REST interfaces.
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No data movement
Data scientists and developers process data where it resides in Oracle Database. This simplifies model building and deployment, reduces application development time, and improves data security.
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High performance compute
Data scientists avoid performance issues during data preparation, model building, and data scoring using the built-in parallelism and scalability of Oracle Database, with unique optimizations for Oracle Exadata.
Introducing Oracle Machine Learning for Python
Mark Hornick, Senior Director, Data Science and Machine Learning, OracleData scientists and developers know the power of Python and Python's wide-spread adoption is a testament to its success. Now, Python users can extend this power when analyzing data in Oracle Autonomous Database. Oracle Machine Learning for Python (OML4Py) makes the open source Python scripting language and environment ready for the enterprise and big data.
Read the complete postResources
Getting started with machine learning in the cloud
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