1.1 What Is Oracle Machine Learning for Python?

Oracle Machine Learning for Python (OML4Py) enables you to run Python commands for data transformations and for statistical, machine learning, and graphical analysis on data stored in or accessible through an Oracle database using a Python API. The OML4Py supports running user-defined Python functions through the database spawned and controlled Python engines, with optional built-in data-parallelism and task-parallelism. This embedded execution functionality enables invoking user-defined functions from SQL, and on ADB, REST. The OML4Py supports Automated Machine Learning (AutoML) for algorithm and feature selection, and model tuning and selection. You can augment the Python included functionality with third-party packages from the Python ecosystem.

OML4Py is a Python module that enables Python users to manipulate data in database tables and views using Python syntax. OML4Py functions and methods transparently translate a select set of Python functions into SQL for in-database execution.

OML4Py is available in the following Oracle database environments:

• OML4Py is available in the Python interpreter in Oracle Machine Learning Notebooks in your Oracle Autonomous Database. For more information, see Get Started with Notebooks for Data Analysis and Data Visualization in Using Oracle Machine Learning Notebooks.

• An OML4Py client connection to OML4Py in an on-premises Oracle Database instance.

  For this environment, you must install Python, the required Python libraries, and the OML4Py server components in the database, and you must install the OML4Py client. See Install OML4Py for On-Premises Databases.

Designed for problems involving both large and small volumes of data, OML4Py integrates Python with the database. With OML4Py, you can do the following:

• Run overloaded Python functions and use native Python syntax to manipulate in-database data, without having to learn SQL.

• Use Automated Machine Learning (AutoML) to enhance user productivity and machine learning results through automated algorithm and feature selection, as well as model tuning and selection.

• Use Embedded Python Execution to run user-defined Python functions in Python engines spawned and managed by the database environment. The user-defined functions and data are automatically loaded to the engines as required, and when data-parallel and task-parallel execution is enabled. Develop, refine, and deploy user-defined Python functions and machine learning models that leverage the parallelism and scalability of the database to automate data preparation and machine learning.

• Use a natural Python interface to build in-database machine learning models.