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.
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.
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.
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.
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.
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.
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.
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.
Dragon Slayer Consulting: Oracle Autonomous Database disrupts database IT outsourcingRead 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 cloudRead the OMDIA report (PDF)
Customers around the world take advantage of Oracle’s in-database machine learning capabilities to solve complex and important data-driven problems.
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.
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.
Developers and the broader data science team achieve immediate machine learning model availability with easy deployment options using SQL and REST interfaces.
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.
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.
Mark Hornick, Senior Director, Data Science and Machine Learning, Oracle
We are pleased to announce the availability of a SQL API for Oracle Machine Learning for Python embedded Python execution on Oracle Autonomous Database. When we announced Oracle Machine Learning for Python on Oracle Autonomous Database early last year, Oracle Machine Learning for Python first supported a REST API for embedded Python execution. This new interface now enables SQL access to this powerful capability.Read the complete post
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