In-Database AI: Machine Learning With OML4SQL
By Bharath Moola, HEXstream data solutions engineer
Oracle Machine Learning for SQL, or OML4SQL, turns your Oracle Database into more than just a place to store data—it lets you do machine learning right where your data lives. Instead of exporting tables to external tools or notebooks, you can train models, test them, and make predictions all with ordinary SQL statements.
The benefits? This approach keeps your data safe inside the database, cuts down on copying files back and forth, and speeds up the process from raw numbers to real insights.
How it works
Behind the scenes, OML4SQL integrates a handful of new, model-focused SQL statements—like CREATE MODEL and DROP MODEL—directly into Oracle’s SQL engine. When you issue a CREATE MODEL, the database’s parallel execution framework automatically splits your training data into chunks and processes them simultaneously across multiple CPU cores (or, in an RAC environment, across several nodes).
This true parallelism slashes training times compared to single-threaded approaches. And if you’ve enabled the Oracle In-Memory option, your tables can live in the In-Memory column store—compressed in columnar format and held entirely in RAM—so that I/O waits all but disappear and your model-building runs at maximum speed.
Rich, built-in algorithms
OML4SQL gives you a wide selection of production-ready algorithms without any extra installations. For classification—like flagging high-risk grid segments—you can choose decision trees (ALGO_DECISION_TREE), random forests (ALGO_RANDOM_FOREST), logistic regression (ALGO_LOGISTIC_REG), support vector machines (ALGO_SVM), or Naïve Bayes (ALGO_NAIVE_BAYES). For numeric forecasting—such as estimating how long an outage will last—you can use linear regression (ALGO_LINEAR_REG) or support vector regression (ALGO_SVR).
To uncover common patterns in outage data—say, grouping incidents by equipment type, location, or cause—k-means clustering (ALGO_KMEANS) and hierarchical clustering help you segment your historical records. And for early-warning systems, one-class SVM (ALGO_SVM_ONE_CLASS) can monitor real-time sensor streams to detect anomalies that often precede unplanned outages.
In-database scoring
Once your model is ready, you don’t need a separate scoring system. You can add a prediction column to any SELECT query and get back results alongside your regular fields. If you’re running nightly reports, you can score millions of rows in one go. If you need near-real-time checks—say, flagging a questionable transaction—you can embed the scoring function directly into your application’s queries.
Everything stays inside the database, which keeps things simple and secure.
Security and governance
Speaking of security, OML4SQL fits right into Oracle’s existing controls. Models are stored in database tables, protected by the same user roles and encryption rules that guard your sensitive data. You get full audit trails that show who built each model and when they did so, and you can manage versions just like you manage table schemas; there’s no need to open another tool or platform to keep track of your machine-learning assets.
Why it matters
In everyday language, OML4SQL makes data science accessible to anyone who knows SQL. It removes the friction of moving data around, cuts down on extra servers or scripts, and brings predictive power into the heart of your operational database.
If you’re already comfortable writing queries, you can quickly add forecasting, classification or anomaly-detection to your toolbox—without learning a whole new environment. The result is faster insights, tighter security, and smoother collaboration between analysts, developers and decision-makers.
