What's new in Cloudera AI on premises 1.5.5

New features and improvements

AI Studios [Technical Preview]

Cloudera AI Studios is a comprehensive suite of low-code tools designed to simplify the development, customization, and deployment of generative AI solutions within enterprises. This suite empowers organizations to operationalize AI workflows quickly and efficiently by leveraging real-time enterprise data.

For more information, see AI Studios Overview.

Cloudera AI Inference service [Technical Preview]

The Cloudera AI Inference service is a data service available in Technical Preview. Cloudera AI Inference service is a production-grade serving environment for traditional, generative AI, and Large Language Models. It is designed to handle the challenges of production deployments, such as high availability, fault tolerance, and scalability. The service is now available to carry out inference on the following categories of models:

• Optimized open-source Large Language Models.
• Traditional machine learning models like classification, regression, and so on. Models need to be imported to the Cloudera AI Registry to be served using the Cloudera AI Inference service.

For more information, see Using Cloudera AI Inference service.

Cloudera Copilot [Technical Preview]

Cloudera Copilot is an AI-powered coding assistant designed for seamless integration within JupyterLab ML Runtimes. With its chat interface and comprehensive code completion features, Cloudera Copilot enhances the development experience for machine learning projects. It offers compatibility with model endpoints deployed in Cloudera AI Inference service model, providing developers with flexibility and efficiency in their workflows.

For details, see Cloudera Copilot Overview.

Model Hub [Technical Preview]

Model Hub is a catalog of top-performing LLM and generative AI models. You can now easily import the models listed in the Model Hub into the Cloudera AI Registry and then deploy it using the Cloudera AI Inference service. This streamlines the workflow of developers working on AI use cases by simplifying the process of discovering, deploying, and testing models.

For more information, see Using Model Hub

Certification Manager

Cert-manager is an open-source tool for Kubernetes that automates the provisioning, management, and renewal of TLS certificates. Its documentation at https://cert-manager.io/docs/ provides comprehensive guidance on installing, configuring, and using cert-manager to secure workloads with trusted X.509 certificates. Cloudera provides out-of-the-box support for Venafi TPP as part of the Cloudera Embedded Container Service installation. By integrating cert-manager, the Cloudera Data Services on premises achieve secure communication, reduced manual overhead, and compliance with security standards, leveraging its robust automation and flexibility. For more information on setting up Cert-manager using Venafi TPP, see Certification Manager service for increased security.

Workbench-level Spark defaults

Custom Spark settings can now be configured at workbench level. When set, the custom Spark configuration provided by the administrator will be merged with the default Spark configuration used in Cloudera AI sessions. These settings will automatically apply to all newly launched Spark sessions within the workbench. The configuration option is available under Site Administration > Runtimes.

Spark pushdown

The Administrator can set Spark pushdown to be enabled during project creation by default.

Auto-synchronization for user and team is enabled by default

The efficiency and usability of the auto-synchronization features have been enhanced for user and team management. Key updates include:

• Auto-synchronization is enabled by default: Auto synchronization for users and teams is now enabled by default, with a synchronization interval set to 12 hours.
• User management service: User management is now handled by a new service, reducing overhead on the web pod. It now prevents multiple synchronization operations from running in parallel.
• Logging: Detailed logging has been added for the failure cases.
• Synchronization trigger sequence: The team synchronization now internally triggers user synchronization to pull the most recent user details from the Cloudera control plane.
• You can switch on or off the User and Team auto synchronization feature.

These improvements are aimed at optimizing performance and streamlining the synchronization process for users and teams.

Multiple docker registry accounts

Cloudera AI now supports storing multiple Docker credentials for your custom runtimes and provides a dedicated UI and API for managing them. Additionally, Cloudera AI no longer retrieves Docker registry credentials from the regcred secret.

If you previously relied on credentials stored in the regcred secret, you must add these credentials to Cloudera AI under Site Administration > Runtime to ensure your workloads continue functioning seamlessly.

UI displays skipped job runs with skipped status tag

Previously, when a job was already running and another job run was triggered by a cron job or an API call, the new run would be skipped and displayed as 'Failed' in the UI. This update introduces a 'Skipped' status, and any skipped job runs will now appear with the 'Skipped' status in the UI.