Increase the productivity of data scientists, data engineers, and developers and reduce their learning curve with familiar notebook technology. Oracle Machine Learning Notebooks supports SQL, PL/SQL, Python, R, Conda, and markdown interpreters for Oracle Autonomous Database so you can work with your language of choice along with custom third-party packages when developing analytical solutions.
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.
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.
Simplify and accelerate the creation of 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.
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.
Accelerate machine learning modeling using Oracle Autonomous Database as a high performance computing platform with an R interface. Use Oracle Machine Learning Notebooks 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.
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 to develop scalable machine learning–based solutions in Python. Built-in AutoML recommends relevant algorithms and features and performs automated model tuning.
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.
Simplify and accelerate the creation of 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 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.
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.
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 algorithms and features and performs automated model tuning.
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.
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 algorithms and features and performs automated model tuning. Together, these capabilities enhance user productivity, model accuracy, and scalability.
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.
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.
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.