Introduction to Oracle Machine Learning for Python (OML4Py)

OML4Py leverages the database as a high-performance computing environment to explore, transform, and analyze data faster and at scale by keeping data in the database, while allowing the use of familiar Python syntax. The in-database parallelized machine learning algorithms are exposed through a well-integrated Python interface. Data Scientists and other users can create user-defined Python functions and manage these as scripts in the database. Python objects can also be stored in the database – as opposed to being managed in flat files. These features facilitate collaboration across the data science team by enabling convenient hand-off of data science work products from data scientists to application developers for immediate deployment. These user-defined functions can be run in a data-parallel or task-parallel manner to enable, for example, scoring native Python models at scale. Results from these user-defined functions can contain both structured and image results and be accessed via Python and REST APIs. OML4Py also supports automated machine learning—or AutoML—which not only enhances data scientist productivity, but also enables non-experts to use and benefit from machine learning. AutoML can help produce more accurate models faster, through automated algorithm and feature selection, and model tuning and selection.