OML Home Page

The Oracle Machine Learning User Interface home page provides you quick links to important interfaces, help links, and the log of your high-level recent activities.

To learn more, see Oracle Machine Learning User Interface Home Page .

Notebooks

Oracle Machine Learning Notebooks is a collaborative web-based interface that supports notebook creation, scheduled run, and versioning. You can document work using Markdown and run SQL, R, and Python code for data exploration, visualization, and preparation, and machine learning model building, evaluation, and deployment. Oracle Machine Learning Notebooks also provide the Conda interpreter to create a custom Conda environment that includes user-specified third-party Python and R libraries.

To learn more, see About Oracle Machine Learning Notebooks.

AutoML User Interface

The AutoML no-code UI simplifies model building for users with limited technical expertise. When you create and run an experiment in AutoML UI, it performs automated algorithm selection, feature selection, and model tuning, thereby enhancing productivity as well as potentially increasing model accuracy and performance.

To learn more, see Get Started with AutoML UI.

OML Models

The Models page displays the user models and the list of deployed models. User Model lists the models in a user's schema, and Deployments lists the models deployed to Oracle Machine Learning Services.

You can access the Models page through the OML user interface. To learn more, see Get Started with Models.

• Data Monitoring

  Data Monitoring evaluates how your data evolves over time. It helps you with insights on trends and multivariate dependencies in the data. It also gives you an early warning about data drift.

  Data drift occurs when data diverges from the original baseline data over time. Data drift can happen for a variety of reasons, such as a changing business environment, evolving user behavior and interest, data modifications from third-party sources, data quality issues, or issues with upstream data processing pipelines.

  To learn more, see Get Started with Data Monitoring.

• Model Monitoring

  Model monitoring allows you to monitor the quality of model predictions over time and helps you with insights on the causes of model quality issues.

  You can access Model Monitor through the OML user interface. To learn more, see Get Started with Model Monitoring.

Oracle Data Miner

Oracle Data Miner (ODMr) is an extension to Oracle SQL Developer. Oracle Data Miner is a graphical user interface to discover hidden patterns, relationships, and insights in data. ODMr provides a drag-and-drop workflow editor to define and capture the steps that users take to explore and prepare data and apply machine learning technology. To learn more, see Oracle SQL Developer. Select your release from the drop-down and click Books to access Oracle Data Miner documents.