Machine Learning Notebooks

Oracle Machine Learning Notebooks provide a collaborative user interface for data scientists and business and data analysts who perform machine learning in Oracle Autonomous Database--both Autonomous Data Warehouse (ADW) and Autonomous Transaction Processing (ATP). Oracle Machine Learning Notebooks enables data scientists, citizen data scientists, and data analysts to work together to explore their data visually and develop analytical methodologies. The Notebooks interface provides access to Oracle's high performance, parallel and scalable in-database implementations of machine learning algorithms via SQL and PL/SQL, with support for Python and R coming soon. Oracle Machine Learning Notebooks uses Apache Zeppelin technology, enabling teams to collaborate to build, assess, and deploy machine learning models. Multi-user collaboration enables the same notebook to be opened simultaneously by different users, such that notebook changes made by one user are instantaneously reflected to notebook viewers.

To support enterprise requirements for security, authentication, and auditing, Oracle Machine Learning Notebooks supports privilege-based access to notebooks, as well as being integrated with Oracle security protocols.

Features Overview

• Collaborative notebook interface for data scientists
• Enables sharing of notebooks and templates with permissions and execution scheduling
• Access to 30+ parallel, scalable Oracle Machine Learning algorithms
• SQL and PL/SQL scripting language supported
• Based on Apache Zeppelin notebook technology--provides a common platform with a single interface
• Multi-user collaboration enables the same notebook to be opened simultaneously by different users, such that changes made by one notebook user are instantaneously reflected to all viewers of that notebook
• For enterprise requirements for security, authentication, and auditing,  supports privilege-based access to data, models, and notebooks, as well as being integrated with Oracle security protocols
• Enables  deployment of enterprise machine learning methodologies in Autonomous Data Warehouse (ADW) and Autonomous Transactional Database (ATP)

For a complete list of features and enhancements, see the product release notes in the documentation.