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, automated machine learning (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.

Announcing Oracle Machine Learning Monitoring on Autonomous Database
Prevent data drift and monitor the performance of your machine learning models. New monitoring capabilities within Machine Learning in Oracle Database services alert you to issues in both data and native in-database model quality.

Read the announcement
Use custom third-party Python and R packages on Autonomous Database
Leverage broader Python and R package ecosystems on Oracle Autonomous Database in Oracle Machine Learning Notebooks. Run user-defined functions with third-party package functionality in engines spawned and managed by the Oracle Database environment.

Read the blog about Use custom third-party Python and R packages on Autonomous Database
Run R at scale on database data with Oracle Machine Learning for R
Explore, transform, and analyze data faster and at scale while using familiar R syntax and semantics and taking advantage of Oracle Database as a high performance computing environment.

Read the blog about Run R at scale on database data with Oracle Machine Learning for R
Embedding AI/ML in your application using Oracle Machine Learning
Deploying and scaling machine learning models and broader Python and R-based solutions in production is often challenging. Learn how to simplify embedding AI and ML in applications using Machine Learning in Oracle Database.

Watch the recording (39:21)

Build high performance machine learning models in Oracle Database

Machine Learning in Autonomous Database

Oracle Machine Learning Notebooks

Increase the productivity of data scientists and developers and reduce their learning curve with familiar open source–based Apache Zeppelin notebook technology. Notebooks support SQL, PL/SQL, Python, R, 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.

How AI Can Take The Drudgery Out Of Tuning Machine-Learning Models

Oracle Machine Learning for R

Accelerate machine learning modeling using Oracle Autonomous Database as a high performance computing platform with an R interface. Use Oracle Machine Learning Notebooks with R, Python, and SQL interpreters to develop machine learning–based solutions. Easily deploy user-defined R functions from SQL and REST APIs with data-parallel and task-parallel options.

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.

GitHub repository

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

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

GitHub repository

AutoML in Oracle Database

Oracle Machine Learning AutoML User Interface

How AI Can Take The Drudgery Out Of Tuning Machine-Learning Models

Oracle Machine Learning for Python

Github repository

No-code User Interfaces for Machine Learning in Oracle Database

Oracle Machine Learning AutoML user interface

How AI Can Take The Drudgery Out Of Tuning Machine-Learning Models

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 how to build machine learning models faster with Python, R, and SQL.

Launch a workshop

See what top industry analysts are saying about us

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)

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.

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

Watch the video (2:09)

Read the story

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.

April 17, 2023

Announcing next-generation Oracle Machine Learning Notebooks on Oracle Autonomous Database

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

We’re pleased to announce the new Oracle Machine Learning Notebooks interface on Autonomous Database—Oracle Machine Learning Notebooks EA—now available in all regions. New features include faster notebook loading times, a new Oracle Redwood look and feel, Jupyter and Zeppelin layouts, richer charting visualizations, and individual paragraph comments and dependencies.

Read the complete post