Oracle Machine Learning for SQL Release 19

Through PL/SQL and SQL APIs, Oracle Machine Learning for SQL (OML4SQL) provides scalable in-database machine learning algorithms. The parallelized algorithms in the database keep data under database control. There is no need to extract data to separate machine learning engines, which adds latency to data access and raises concerns about data security, storage, and recency.

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.

Oracle Machine Learning for SQL Release 19

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