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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.

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.

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

Dragon Slayer Consulting: Oracle Autonomous Database disrupts database IT outsourcing

Read the Dragon Slayer Consulting report (PDF)
Enterprise Strategy Group

Enterprise Strategy Group finds Oracle’s Autonomous Data Warehouse enhancements “democratize simplicity”

Read the Enterprise Strategy Group blog
OMDIA: Oracle is the only vendor report

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)
View all customer successes

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.

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NHS Customer logo

UK NHS saves £1.6 billion and provides better personalized care with Machine Learning in Oracle Database.

Benefits

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

February 23, 2021

Introducing Oracle Machine Learning for Python

Mark Hornick, Senior Director, Data Science and Machine Learning, Oracle

Data 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 post

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