1 What's New in Oracle Machine Learning User Interface on Autonomous Database

Provides a summary of the latest enhancements and features for Oracle Machine Learning User Interface on Oracle Autonomous Database.

Table 1-1 New Features

Features	Description
Support for model monitoring in Oracle Machine Learning User Interface	Oracle Machine Learning User Interface supports for model monitoring. It allows you to create model monitors using which you can monitor the quality of model predictions over time, and provides you with insights on the underlying causes.
Support for data monitoring in Oracle Machine Learning User Interface	Oracle Machine Learning User Interface offers support for data monitoring. It allows you to monitor your data and evaluate how your data evolves over time. It helps you with insights on trends and multivariate dependencies in the data. It also provides you an early warning about data drift.
Support for enhanced notebooks in Autonomous Database - Serverless	Oracle Machine Learning User Interface offers a new enhanced notebook environment Notebooks EA (Early Adopter) in Autonomous Database - Serverless. The enhanced notebook supports SQL, SQL Script, R, Python, Conda, and Markdown interpreters. You can write code, text, create rich visualizations, and perform data analytics including machine learning in the enhanced notebooks. Note: The enhanced notebook is available in the Oracle Machine Learning Notebook Early Adopter release. During the Early Adopter release period, both Zeppelin and the enhanced notebooks will be available, after which all notebooks will be converted to the new notebook environment. During the Early Adopter phase, you can use both the original Zeppelin and new Early Adopter notebook interfaces. Notebooks in the original interface can be copied to the Early Adopter release. The enhanced notebook interface in Autonomous Database - Serverless provides the following enhanced features and user experiences: Rich and enhanced user experience: The enhanced notebook offers modern look and feel, and richer visualization with many charting options. This will benefit users to better visualize and understand their data. In addition, it offers some useful features like side-by-side versions comparison, option to add comments to paragraphs, full screen size mode for paragraphs, option to define paragraph dependency, and so on. High availability: The enhanced notebook, a multi-tenant application is deployed to the same middle-tier as Oracle Machine Learning server, and this requires no additional resources. Therefore, it is always running and readily available to render the new enhanced notebooks. High scalability: The enhanced notebook assures high scalability in production. To scale up due to increased user demands, additional notebook instances can be easily added. There are tools to monitor system loads, and if a system is consistently overloaded, additional instance can be easily added to mitigate risks related to scalability.
Support for Python and R third-party libraries	Third-party libraries for Python and R are available on Oracle Machine Learning Notebooks. Oracle Machine Learning UI provides the Conda interpreter to install third-party Python and R libraries inside a notebook session. Conda is an open-source package and environment management system that enables the use of environments containing third-party Python and R libraries. Users with OML_SYS_ADMIN role can install Python and R third-party libraries and upload them to object storage for persistence. The user with OML_SYS_ADMIN role is the administrator, also known as the admin. Users with OML_DEVELOPER role can use the Conda interpreter to download and activate the third-party libraries using the Conda environment that are provisioned by the administrator. The user with OML_DEVELOPER role is the regular Oracle Machine Learning user.
Support for R	Oracle Machine Learning for R is supported within Oracle Machine Learning Notebooks. By using Oracle Machine Learning for R, you can perform data exploration and machine learning modeling. OML4R is available through Oracle Machine Learning Notebooks on Oracle Autonomous Database - Serverless, including Autonomous Data Warehouse , Autonomous Transaction Processing and Oracle Autonomous JSON Database services.
Support for cross-region Autonomous Data Guard	Oracle Machine Learning Notebooks provide cross-region Autonomous Data Guard support in newly provisioned and migrated databases.
Oracle Machine Learning repository migrated from Serverless database to each respective Oracle Autonomous Database instance.	The Oracle Machine Learning (OML) repository has been migrated from Serverless database to each respective Oracle Autonomous Database instance. The migration of the Oracle Machine Learning repository ensures: That all OML objects such as tables, jobs, stored procedures, and metadata are moved to the appropriate Oracle Autonomous Database instance. Provides support for Refreshable Clones, which enables cloning of the Oracle Machine Learning metadata as well. Note: The migration of the Oracle Machine Learning (OML) repository is expected to be completed over a period of 30 days. The OML repository version is mentioned in About in the <user> drop-down list on the top right corner of your Oracle Machine Learning User Interface page. If the version is 1.0.0.0.0, it indicates that the OML metadata is still in the Serverless database. If the version is 22.x, it indicates that the OML repository has been migrated to your Oracle Autonomous Database instance.
Oracle Machine Learning Notebook supported on all Oracle Autonomous Database clones	Oracle Machine Learning Notebook is supported on all types of Oracle Autonomous Database - Serverless clones, including: Full Clone: a new database is created with the data in the source database and metadata. Refreshable Clone: a read-only full clone is created that can be easily refreshed with the data from the source database Metadata Clone: a new database is created that includes all of the source database schema metadata, but not the source database data. Note: For a metadata clone, the Example Template notebooks are not supported.