3 New Features, Enhancements and Limitations in this Release

New Features
  • Installer Upgrades
    • The default installation values for configurations related to connection pools, thread pools, job cleanups, and session timeouts are now optimized to support higher load and concurrency.
  • General Features
    • API Registry - Added support for basic authentication actions.
  • File Manager
    • Enhanced auditing for all file operations.
    • Users can now bookmark the folders of their choice as a favourite for quick access.
    • File manager now opens the last opened directory by default.
  • Workspace
    • Audit Trail now captures workspace related operations.
  • Datasets

    • Pushdown filtering is now implemented in the dataset widget in the pipelines feature.

  • Model Pipelines
    • Functionality to pull or push objectives or drafts from one workspace to another.
    • Self-approval of model deployments can now be restricted at the user-level.
    • Session based filtering in the Executions tab of the pipeline, where the user can view the list of executed sessions and also access to the notebook view that is in session.
    • Validations are introduced to check the cyclic loops in the canvas with an option to highlight cycle-inducing links and flattening as per the notebook order.
    • Users can now add comments at a paragraph-level in notebooks that will also get audited in the review process.
    • A new widget is added to perform windowed computations (tumbling or sliding), with various configurations on event streams flowing from Kafka.
  • Performance Improvements
    • Reduced number of calls and time taken for workspace attachment during the notebook initialization in the model pipelines to decrease latency.
    • Improved the First Content Paint (FCP) time that was the result of decreasing the synchronous calls to the server from the client along with the implementation of the async ajax calls. This has decreased the overall latency.
    • GZIP compression: 90% compression is done on eligible responses, significantly decreasing bandwidth usage.
    • Image Optimization: Optimized all images across all modules, thus reducing the network overload for images.
  • Technology Stack
    • Python support has been upgraded form 3.10 to 3.12. The OFS MMG python library stack does not support Python 3.9.
    • The application now carries the following updated versions:
      • Data Studio 25.4.1-1
      • All 3rd party libraries upgraded to latest stable versions for improved performance and security.
Limitations from Past Releases that have been Remediated in this Release
  • Graceful cleanup of data model jobs in case of any abrupt shutdown of services is now handled.
Known Issues and Limitations
  • Tables deletion sync-up between schemas is not supported during workspace edits.
  • Error log table creation fails if the column data types are LONG, CLOB, BLOB, BFILE, and ADT during the workspace data population.
  • Unable to perform the dataset cache action with the model library.
  • The PDF of the model report does not contain data in the output section.
  • Deployment of models that are published from the model summary screen will not promote the associated dependencies such as Graphs, Parameter Sets, Datasets, and Models. However, the same works fine, if the models are published from within the pipeline UI.
  • As of now, Python 3.12 does not support the apache-flink completely, hence, installing Python 3.12 might display a few errors.

  • Oracle-guardian-ai is no longer a mandatory library installed by mmg-python library. Instead it needs to be installed and configured in a dedicated conda environment. This is due to the limitation of the oracle-guardian-ai version compatibility and no support for python 3.12.