Cloudera Docs

February 19, 2026

Release notes and fixed issues for Cloudera AI Workbench 2.0.55-b193, Cloudera AI Registry 1.13.0-b41, and Cloudera AI Inference service 1.10.0-b91 provide functional updates across these components and resolutions for identified bugs.

New Features / Improvements

Cloudera AI Workbench
  • Improved application responsiveness by optimizing database query performance across projects and jobs, significantly reducing page load times for environments with large datasets.
Cloudera AI Control Plane
  • Improved consistency across the administrative experience for AI Workbench, AI Registry, and the AI Inference services.
  • Added support for Amazon EKS 1.33.
  • Added support for Azure AKS 1.33.
  • Improved the reliability of Cloudera AI Inference service upgrade processes.
  • Cloudera AI now automatically performs side-by-side upgrades for incompatible environments, ensuring a seamless one-click experience with built-in rollback support in case of failure. To upgrade a workbench, Cloudera users must hold both the MLAdmin and EnvironmentAdmin roles, and those performing an upgrade must also possess the necessary cloud provider permissions to execute backup and restore operations for the underlying storage and metadata databases. For more information, see Upgrading Cloudera AI Workbenches.
Cloudera AI Inference service
  • Cloudera AI Inference service now provides a production-grade serving environment for hosting applications. Applications deployed on Cloudera AI Inference service can scale alongside Model Endpoints, providing a scalable solution for various components. For more information, see Serving Applications on Cloudera AI Inference service (Technical Preview) .
  • Cloudera AI Inference service now supports AWS on-demand capacity reservations and capacity blocks to ensure compute availability for inference workloads. For more information, see Configuring AWS on-demand capacity reservations and capacity blocks.
  • Cloudera AI now supports the deployment of Hugging Face reranking models using the API.
  • Cloudera AI now supports deploying Hugging Face embedding models using the API.
  • You can now manually specify model tasks (such as EMBED, RANK, or CLASSIFICATION) using API during deployment, enabling broader vLLM support for architectures like bertmodel or modernbertfortokenclassification that serve tasks like embedding and reranking respectively.
  • You can now manage Cloudera AI Inference service logging globally using the Serving API ConfigMap, allowing administrators to enable logging and define a storage bucket across all endpoints simultaneously for consistent data collection.
  • Added Fine Grained Authorization support. For more information, see Configuring Fine-grained Access Control.
Cloudera AI Registry
  • Cloudera AI Inference service now supports direct deployment for XGBoost, PyTorch, and TensorFlow models using the AI Registry. For more information, see Deploying Additional Model Frameworks.
  • Cloudera AI Registry now displays structured metadata and comprehensive lineage tracking (provider, model ID, and SHA) for all models imported from Hugging Face and NVIDIA NGC.

Fixed Issues

Cloudera AI Workbench
  • Resolved an issue that prevented Team Administrators from updating the team description in the UI. (DSE-48770)
  • Fixed the workload status synchronisation issue between dashboards and dashboard_pods, ensuring accurate and consistent resource usage reporting. (DSE-46977)
Cloudera AI Inference service
  • Resolved an issue where Hugging Face model IDs in inference URLs were un-sanitized, causing routing failures; inference endpoints now correctly use underscores (_) instead of slashes to align with Triton directory structures and authorizer patterns. (DSE-50543)
  • Fixed an issue where Hugging Face embedding models failed to deploy to vLLM due to an unsupported embed task key. (DSE-49993)