About Oracle Machine Learning for SQL

Oracle Machine Learning for SQL (OML4SQL) provides scalable in-database machine learning algorithms through PL/SQL and SQL APIs. The algorithms are fast and scalable, support algorithm-specific automatic data preparation, and can score in batch or real-time.

OML4SQL provides a powerful, state-of-the-art machine learning capability within Oracle Database. 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. You can use OML4SQL to build and deploy predictive and descriptive machine learning applications, to add intelligent capabilities to existing applications, and to generate predictive queries for data exploration. OML4SQL provides explanatory prediction details when scoring data, so you can understand why an individual prediction is made.

OML4SQL offers a broad set of in-database algorithms for performing a variety of machine learning tasks, such as classification, regression, anomaly detection, feature extraction, clustering, and market basket analysis. The algorithms can work on standard case data, transactional data, star schemas, and unstructured text data. OML4SQL is uniquely suited to the analysis of very large data sets.

Oracle Machine Learning for SQL, along with Oracle Machine Learning for R and Oracle Machine Learning for Python, is a component of Oracle Machine Learning that provides three powerful APIs for in-database machine learning, among other features.