Cloudera Docs

What's New

Major features and updates for the Cloudera AI data service.

July 31, 2025

Release notes and fixed issues for version 2.0.52-b27.

New Features / Improvements

Cloudera AI Platform
  • Added support for file storage replication in AWS EFS (Elastic File System), enhancing data redundancy and availability. For information, see Configuring File Storage Replication on AWS.
  • Improvements have been made to enable retriable in-place upgrades, leading to more robust upgrade processes.
  • For information, see Upgrading Cloudera AI Workbenches.
  • Added support for Azure regions: Poland Central and Italy North.
  • Added support for Istio.
  • Added support for Customer-Managed Key (CMK) encryption in Cloudera AI Azure workbenches. For information, see Enabling Customer Managed Keys on Microsoft Azure.
  • You can now change Persistent Volume Claim (PVC) sizes through both the UI on the Workbench Details page and using the CDP CLI. For information, see Modifying workbench persistent volume size.
  • Added a liveness probe to the mlx crud application pod and implemented graceful shutdown, improving the stability and resilience of the application.
  • The system will now automatically select the Cloudera AI Registry if only one instance exists within a given tenant.
  • Improvements have been made to the UI, allowing you to create Cloudera AI Registries with private clusters and enable User-Defined Routing (UDR) more easily.
  • Added user-friendly information to the UI to assist users when utilizing the air-gapped model hub import functionality.
Cloudera AI Registry
  • Added CSI driver support for AI Registries, removing previous resource constraints. You can now download any number of models in parallel without encountering resource limitations with Azure.
  • User-friendly and informative error messages are now displayed when users are unable to import a model to the AI Registry.
  • A new caching mechanism has been introduced in Model Hub, significantly reducing the time it takes for pages to load.
Cloudera AI Inference service
  • Added support for the Nemotron Super 49B model.
  • Added support for Riva ASR NIM (NVIDIA Inference Microservice), enabling advanced automatic speech recognition. This feature is compatible with the Whisper mode, requiring a 16-bit, mono, 16000 Hz, uncompressed WAV file as input.
  • Added support for several new vLLM load formats, including sharded_state, gguf, bitsandbytes, mistral, runai_streamer, and fastsafetensors. This enhances the list of supported vLLM quantization options.
  • Nemotron's thinking mode is now user-configurable, allowing you to explicitly activate this advanced reasoning capability by including "content": "detailed thinking on" within the system role of your prompt payload, giving you precise control over resource usage.
  • Implemented necessary validators for GPU instance types during the deployment of NVIDIA models to prevent misconfigurations.
  • Significantly improved the performance of Cloudera AI Inference service by caching tokens to improve UI responsiveness and decrease network load.
  • The replica for endpoint logs and events is now automatically selected for any given model endpoint.
  • Added a Refresh button to various sub-sections of the model endpoint details page for easier data updates.
  • A force fetch button is now available on the Model Hub UI for users to override cached values and ensure the latest data is displayed.
  • Replaced generic Failed to Fetch messages with more user-friendly error messages when a user attempts to import a Hugging Face model not present in our Model Hub.
  • An alert box is now displayed in the UI to notify users when an Ingress Ready endpoint has a replica count of 0.
ML Runtimes
  • Resource requests for several core Cloudera AI services have been increased. This change is designed to boost performance and stability, ensuring a smoother experience without requiring any action on your part.

Fixed Issues

Cloudera AI Platform
  • Resolved an issue that prevented Cloudera AI Registries from being visible in the control plane after their certificates were renewed. (DSE-44836)
  • Previously, the configuration map of Cloudera AI Inference service was not updating correctly during the upgrade process. This issue is now resolved. (DSE-45417)
Cloudera AI Registry
  • Previously, constraints in the UI prevented upgrading an AI Registry already in a Ready status. This issue is now resolved. (DSE-45663)
Cloudera AI Workbench
  • Addressed an issue due to which registries whose certificates were renewed were not visible from within the workbench. (DSE-44837)
Cloudera AI Inference service
  • Resolved an issue that prevented the configuration map of AI Inference from updating correctly during the upgrade process. (DSE-45417

  • Resolved an issue encountered when importing the A10G profile of the Llama 3.2 rerank 1B model. (DSE-45375)

  • Previously, copying the base URL of model endpoints from the UI did not work properly, as the wrong link was getting copied. This issue is now resolved.(DSE-45107 and DSE-45534)
  • Resolved an issue that prevented rendering of Test Model and code samples for external Hugging Face models, such as Gemma 3. (DSE-45419)
  • Addressed inconsistencies observed in the Summary and Details metrics displayed for Model Endpoints. (DSE-45185)
  • Fixed an issue where the Cloudera AI Registry upgrade pop-up was not correctly being dismissed. (DSE-46224).
  • Previously, when the Test Model under Model Endpoint was executed, the UI blocked navigation to other tabs, such as Metrics, Logs, and so on. This issue is now resolved. (DSE-46247)
  • Previously, due to a known KServe issue (kserve/kserve#4471), all newly created model endpoints would initially deploy with a single replica, regardless of the specified configuration. This issue is now resolved. (DSE-45876)
ML Runtimes
  • Previously, PBJ Workbench-based workloads kept running when the underlying kernel had stopped or restarted. This is now fixed, and PBJ Workbench-based workloads will terminate if the underlying kernel terminates. (DSE-42964)
  • Previously, messages printed from PBJ Workbench-based models did not appear in the model logs. This issue is now resolved. (DSE-42960)
  • Previously, in the PBJ Workbench editor where text with special styling (for example, colored fonts) lost its formatting in the console. This issue is now resolved. (DSE-42958)
  • Previously, PBJ Workbench Runtimes did not stop when "exit" was executed in a Python kernel or when "quit()" was executed in an R kernel. This issue is now resolved, and now these commands terminate the workload as expected. (DSE-36835)
  • Fixed the known issue related to Spark executors in R. Now the environment variable R_LIBS_USER has the same configuration in Spark executors as in other workloads. It is no longer needed to manually configure R_LIBS_USER for Spark executors in R. (DSE-32839)
  • Previously, some types of output were not fully shown in the Workbench UI when the workload was running a PBJ Workbench Runtime. This issue is now resolved. (DSE-43865)

June 06, 2025

Release notes and fixed issues for version 2.0.50-b68 and MLX-CRUD-APP version 1.50.0-b139.

New Features / Improvements

Cloudera AI Platform
  • Added support for Azure AMD Easv5 instances. (DSE-38566)

May 20, 2025

Release notes and fixed issues for version 2.0.50-b68.

New Features / Improvements

Cloudera AI Workbench
  • 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 Managing AI Studios.
  • Added APIs within the workbench to list Cloudera AI Inference service applications and their associated model endpoints.
Cloudera AI Platform
  • Added Azure UDR support for Cloudera AI Inference service.
  • Added Azure NTP support.
  • Added API support to retry the creation of Cloudera AI Inference service application upon failure.
Cloudera AI Registry
  • Added a new set of models in the Model Hub, including Llama3.3, DeepSeek-R1-Distill-Llama, Starcorder2, Llama-Nemotron-Nano, NeMo-Retriever-Parse, Llama 3.2 Embedding, and Llama 3.2 Encoder models. To access these models, you must upgrade your Cloudera AI Registries.
  • Added support for nim-cli in the AI Registry to import the latest offerings from NVIDIA.
  • Enhanced troubleshooting by surfacing underlying issues encountered during AI Registry installation in the Event logs.
  • Provided the ability to upgrade the AI Registry directly through the UI, eliminating the reliance on the CLI.
  • Implemented automatic redirection to the model import status page whenever a new model import is triggered.
Cloudera AI Inference service
  • Users must upgrade their Cloudera AI Inference service applications to serve the latest optimized models from NVIDIA, including Llama3.3, DeepSeek-R1-Distill-Llama, Starcorder2, Llama-Nemotron-Nano, NeMo-Retriever-Parse, Llama 3.2 Embedding, and Llama 3.2 Encoder models.
  • Optimization profile details for deployed model endpoints are now surfaced in the UI for improved visibility.
  • A user-friendly warning message will now be displayed when replicas of a deployed model scale down.
  • Added an option in the UI to retry the creation of Cloudera AI Inference service applications.
  • Users will be automatically redirected to the model endpoint page upon triggering the deployment of a new endpoint.
  • Enhanced the UI with a variety of user-friendly tooltips for better usability.
  • The metrics page for model endpoints will now refresh automatically every 15 seconds for real-time updates.
  • GPU count is now auto-selected for NIM profiles when deploying a model endpoint.
  • Ensured that dangling pods of deleted endpoints are immediately terminated, preventing them from being left for garbage collection cleanup.

Fixed Issues

Cloudera AI Workbench
  • Resolved an issue where duplicate machine user CRNs were preventing the catalog page backup from loading. (DSE-43729)
  • Fixed an invalid error issue in the Cloudera AI Registry search filter within the workbench. (DSE-44401)
Cloudera AI Platform
  • Resolved the issues causing failures during the retry of upgrade operations. (DSE-44761)
  • Resolved an issue where team synchronization was removing all collaborators for a project created with LDAP team when a member was removed from the UMS group mapped to that LDAP team. (DSE-43524)
Cloudera AI Inference service
  • Resolved an issue causing the deletion of the incorrect node group from the Cloudera AI Inference service UI. (DSE-44981)
  • Resolved an issue preventing the import of older models like Mixtral due to compatibility constraints. (DSE-44972)