Explanation
https://docs.oracle.com/en/cloud/paas/autonomous-database/adbsa/autonomous-oml.html#GUID-63F2D68B-6D

* The data preparation step for analyzing data with Oracle Machine Learning involves various actions to transform the raw data into a suitable format for machine learning algorithms45.
* Some of the actions that would typically be performed during the data preparation step are:
* Numeric data normalization: This is a technique for reducing the range of numerical data by mapping them to a standard scale, such as 0 to 1. Normalization can improve the performance and stability of some machine learning algorithms5.
* Missing value replacement: This is a technique for handling missing or null values in the data, which can cause errors or bias in some machine learning algorithms. Missing values can be replaced by various methods, such as mean, median, mode, or a constant value4.
* Performing feature engineering, such as creating derived variables: This is a technique for creating new features from existing ones or combining them in meaningful ways. Feature engineering can enhance the predictive power and interpretability of machine learning models4.
* Binning of numeric data: This is a technique for reducing the cardinality of continuous and discrete data by grouping related values together in bins. Binning can improve resource utilization and model build response time without significant loss in model quality. Binning can also strengthen the relationship between attributes and improve model quality5

Explanation
https://blogs.oracle.com/machinelearning/post/introducing-oml-automl-user-interface
https://www.oracle.com/a/tech/docs/technical-resources/oml-technical-brief.pdf

* The three Oracle Machine Learning (OML) components that are available with Oracle Autonomous Database are OML Notebooks, OML4SQL, and OML Services2.
* OML Notebooks are web-based interfaces for data analysis, data discovery, data visualization, and collaboration based on Apache Zeppelin notebook technology. They support SQL, PL/SQL, Python, R,
* and markdown interpreters on Oracle Autonomous Database2.
* OML4SQL is a set of in-database machine learning algorithms and SQL functions that enable data exploration, data preparation, model building, evaluation, and deployment using SQL and PL/SQL on Oracle Autonomous Database2.
* OML Services are RESTful web services that enable users to build, deploy, and manage machine learning models using REST APIs on Oracle Autonomous Database. They support native in-database models and ONNX-format models for classification, regression, and clustering2

Explanation
https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/output-formats-supported-set