Oracle Machine Learning Notebooks is a collaborative user interface for data scientists and business and data analysts who perform machine learning in Oracle Autonomous Database. OML Notebooks enables you to explore data visually and develop analytical methodologies in Autonomous Database. Through OML Notebooks, you can work with R and OML4R, but also Python OML4Py, SQL, PL/SQL, and OML4SQL.
Oracle's high performance, parallel and scalable in-database implementations of machine learning algorithms are exposed via SQL, PL/SQL, R, and Python APIs. Oracle Machine Learning enables teams to collaborate to build, assess, and deploy machine learning models, while increasing data scientist productivity Oracle Machine Learning focuses on ease of use and simplified machine learning for data science – from preparation through deployment – all in Oracle Autonomous Database and Oracle Database.
Multi-user collaboration enables the same notebook document to be opened simultaneously by different users, such that changes made by one user to a notebook are reflected to all users viewing that notebook. To support enterprise requirements for security, authentication, and auditing, Oracle Machine Learning supports privilege-based access to data, models, and notebooks, as well as being integrated with Oracle security protocols.
Collaborative UI
- Data scientists, developers and DBAs use built-in SQL, R, Python, conda, and markdown interpreters
- Import/export Zeppelin/Jupyter format notebooks
- Schedule, version, and control access to notebooks
- Add comments on individual notebooks paragraphs
Included with Autonomous Database
- Automatically provisioned and managed
- Use in-database algorithms and analytics functions
- Explore and prepare data, build and evaluate models, score data, and deploy solutions
- Create conda environments with third-party R and Python packages and use in notebook paragraphs
The examples here cover a range of functionality and methods, from data preparation and data cleansing to machine learning modeling and solution deployment.
The specific denomination "21c or 23ai" in the name of the file means that the algorithm used in that demo is supported on that release only.
