• A false statement regarding Oracle Machine Learning (OML) is that OML offerings need a separate data visualization tool for creating visualization56.
• OML does not need a separate data visualization tool for creating visualization because it provides various options for visualizing data and models within its offerings. For example, OML Notebooks support interactive charts and graphs using Plotly and Matplotlib libraries for Python and R. OML SQL also supports native SQL functions for creating histograms, scatter plots, box plots, and more

• 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

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