Oracle Machine Learning for SQL Release 23
The algorithms are fast and scalable, support algorithm-specific automatic data preparation, and can score in batch or real-time. OML4SQL provides explanatory prediction details when scoring data, so you can understand why an individual prediction is made. Furthermore, Oracle's Exadata Smart Scan technology moves scoring processing to the data storage tier, resulting in significant performance gains when scoring data.
In-database machine learning models are first-class database objects. You can manage access by granting and revoking permissions, auditing user actions, and exporting and importing machine learning models across databases. With in-database models, deployment is instantaneous through SQL queries that use SQL prediction operators. OML4SQL reduces solution complexity significantly.
Get Started
![](sp_common/shared-images/gs_64_getstart.png)
Learn About OML4SQL
![](sp_common/shared-images/gs_64_use_examples.png)
OML4SQL Examples
![](sp_common/shared-images/gs_64_tasks.png)
![](sp_common/shared-images/gs_64_resources.png)