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Machine Learning in Oracle Database Features

Features topics

Machine Learning in Oracle Autonomous Database

Machine Learning Notebooks

Increase your productivity, whether you’re a data scientist, data engineer, or developer. Oracle Machine Learning (OML) Notebooks supports SQL, PL/SQL, Python, R, Conda, and markdown interpreters, so you can work with your language of choice alongside in-database machine learning and custom third-party packages to develop analytical solutions. Collaborate with your broader data science team, schedule notebooks to run automatically, visualize your data, and version and compare notebooks with this built-in notebooks environment.

Machine Learning Services

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. Monitor your data and in-database models to ensure ongoing correctness and accuracy. Deploy models quickly and easily from the Oracle Machine Learning AutoML User Interface.

Machine Learning Monitoring

Gain insights into how your enterprise data evolves over time and take corrective action before data issues have a significant negative impact on the enterprise. Data monitoring helps you ensure data integrity for your enterprise applications and dashboards. Quickly and reliably identify data drift and understand individual data columns and their interactions. Model monitoring helps identify when your model metric, such as accuracy or R-squared, significantly changes—or the distribution of predicted values deviates too much from initial values. This may signal the need to rebuild or redesign your model. The no-code data and model monitoring UI provides multiple visualizations and metrics to aid users in assessing quality issues.

Machine Learning for SQL

Simplify and accelerate the creation of in-database machine learning models by both expert data scientists and nonexpert users with SQL and PL/SQL for data preparation and model building, evaluation, and deployment.

Machine Learning AutoML User Interface

A no-code user interface supports AutoML on Oracle Autonomous Database to improve both data scientist productivity and nonexpert user access to powerful in-database algorithms for classification and regression. Rapidly experiment with data, algorithms, and hyperparameters for faster exploration and discovery. Deploy models immediately through SQL queries or to OML Services as REST endpoints for seamless integration with applications and real-time scoring. Generate notebooks for selected models, allowing users to further refine and customize models within OML Notebooks.

Machine Learning for R

Oracle Machine Learning for R (1:38)

Accelerate machine learning modeling using Oracle Autonomous Database as a high performance computing platform with an R interface. Use Oracle Machine Learning Notebooks or your favorite R IDE to develop scalable machine learning–based solutions in R and create Conda environments with third-party packages. Easily deploy user-defined R functions from SQL and REST APIs with system-provided data parallelism and task parallelism.

Machine Learning for Python

Create an Autonomous Database and tour the service (1:37)

Data scientists and other Python users accelerate machine learning modeling and solution deployment by using Oracle Autonomous Database as a high performance computing platform with a Python interface. Use Oracle Machine Learning Notebooks or your favorite Python IDE to develop scalable machine learning–based solutions in Python. Built-in AutoML recommends relevant in-database algorithms and features and performs automated model tuning and selection.

Data Miner

Data scientists and data analysts can use this drag-and-drop user interface to quickly build analytical workflows within SQL Developer. Rapid model development and refinement let users discover hidden patterns, relationships, and insights in their data.

Machine Learning in Oracle Database

Machine Learning for SQL

Simplify and accelerate the creation of in-database machine learning models for both expert data scientists and nonexpert users with SQL and PL/SQL for data preparation and model building, evaluation, and deployment.

Data Miner

Data scientists and data analysts can use this drag-and-drop user interface to quickly build analytical workflows. Rapid model development and refinement let users discover hidden patterns, relationships, and insights in their data.

Machine Learning for R

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 system-provided data parallelism and task parallelism. User-defined R functions can include functionality from the R package ecosystem.

Machine Learning for Python

Data scientists and other Python users accelerate machine learning modeling and solution deployment by using Oracle Database as a high performance computing platform with a Python interface. Built-in AutoML recommends relevant in-database algorithms and features and performs automated model tuning and selection.

AutoML

Machine Learning AutoML User Interface

A no-code user interface supports AutoML on Oracle Autonomous Database to improve both data scientist productivity and nonexpert user access to powerful in-database algorithms for classification and regression. Rapidly experiment with data, algorithms, and hyperparameters for faster exploration and discovery. Deploy models immediately through SQL queries or to OML Services as REST endpoints for seamless integration with applications and real-time scoring. Generate notebooks for selected models, allowing users to further refine and customize models within OML Notebooks.

Machine Learning for Python

Data scientists and other Python users accelerate machine learning modeling and solution deployment by using Oracle Autonomous Database and Oracle Database as a high performance computing platform with a Python interface. Built-in AutoML recommends relevant in-database algorithms and features and performs automated model tuning and selection. Together, these capabilities enhance user productivity, model accuracy, and scalability.

No-code user interfaces

Machine Learning AutoML User Interface

A no-code user interface supports AutoML on Oracle Autonomous Database to improve both data scientist productivity and nonexpert user access to powerful in-database algorithms for classification and regression. Rapidly experiment with data, algorithms, and hyperparameters for faster exploration and discovery. Deploy models immediately through SQL queries or to OML Services as REST endpoints for seamless integration with applications and real-time scoring. Generate notebooks for selected models, allowing users to further refine and customize models within OML Notebooks.

Machine Learning Monitoring

Gain insights into how your enterprise data evolves over time and take corrective action before data issues have a significant negative impact on the enterprise. Data monitoring helps you ensure data integrity for your enterprise applications and dashboards. Quickly and reliably identify data drift and understand individual data columns and their interactions. Model monitoring helps identify when your model metric, such as accuracy or R-squared, significantly changes—or the distribution of predicted values deviates too much from initial values. This may signal the need to rebuild or redesign your model. The no-code data and model monitoring UI provides multiple visualizations and metrics to aid users in assessing quality issues.

Data Miner

Data scientists and data analysts can use this drag-and-drop user interface to quickly build analytical workflows. Rapid development and refinement let users discover hidden patterns, relationships, and insights in their data.