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19 releases of 1321

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  1. Genie Code for AI Runtime (Public Preview)

    Generates distributed training code and resolves environment issues. Requires AI Runtime.

    #Genie Code#Machine Learning
    AWS, read it on their docsAzure, read it on their docsGCP(not on this cloud)SAP(unknown)
  2. Custom trace views in the MLflow trace explorer are in Beta

    Genie generates views based on plain language descriptions, surfacing relevant trace fields and metrics.

    #Gen AI#MLflow
    AWS, read it on their docsAzure, read it on their docsGCP, read it on their docsSAP(unknown)
  3. Organize your work with spaces

    Preserves open tabs for specific folders or projects.

    #Notebooks#Workspace
    AWS, read it on their docsAzure, read it on their docsGCP, read it on their docsSAP(unknown)
  4. Web terminal on serverless GPU compute (AI Runtime) is in Public Preview

    Runs shell commands, monitors GPU usage, and manages files on serverless GPU compute. Requires environment version 5 or above.

    #Compute#Machine Learning
    AWS, read it on their docsAzure, read it on their docsGCP(not on this cloud)SAP(unknown)
  5. Create a least-privilege Databricks workspace

    Grants Databricks a narrow set of custom IAM roles instead of broad permissions on Google Cloud.

    #Security#Workspace
    AWS(not on this cloud)Azure(not on this cloud)GCP, read it on their docsSAP(not on this cloud)
  6. MLflow trace storage in Unity Catalog is now generally available

    Stores traces in OpenTelemetry format with unlimited storage and SQL query access.

    #MLflow#Unity Catalog
    AWS, read it on their docsAzure, read it on their docsGCP, read it on their docsSAP(not on this cloud)
  7. Run the change, do not just read it. Hands-on labs in your own Databricks workspace, graded when you submit. Browse labs
  8. Protobuf tensor input for custom model serving endpoints (Public Preview)

    Replaces JSON with serialized KServe v2 ModelInferRequest. Requires endpoints deployed after July 9, 2026.

    #Machine Learning#Model Serving
    AWS, read it on their docsAzure, read it on their docsGCP, read it on their docsSAP(not on this cloud)
  9. Custom Docker images for AI Runtime CLI workloads (Beta)

    Allows custom system library versions and complex dependencies, requires setup via Use custom Docker images.

    #Machine Learning
    AWS, read it on their docsAzure, read it on their docsGCP(not on this cloud)SAP(not on this cloud)
  10. AI Runtime CLI (Beta)

    Submits and manages distributed training workloads on serverless GPU compute from a local machine.

    #Machine Learning
    AWS, read it on their docsAzure, read it on their docsGCP(not on this cloud)SAP(not on this cloud)
  11. Serve custom LLMs with Custom Model Serving (Beta)

    Supports multimodal models and PEFT recipes, unlike Foundation Model APIs.

    #Machine Learning#Model Serving
    AWS, read it on their docsAzure, read it on their docsGCP(not on this cloud)SAP(not on this cloud)
  12. Customer-managed keys now support MLflow managed evaluation features

    Supports MLflow 3 scorers and requires non-CMK encrypted catalog.

    #MLflow#Security
    AWS, read it on their docsAzure, read it on their docsGCP, read it on their docsSAP(not on this cloud)
  13. MLflow trace storage in Unity Catalog is now in Public Preview

    Stores OpenTelemetry traces, governed by Unity Catalog permissions, queryable from Databricks SQL or MLflow Python SDK.

    #MLflow#Unity Catalog
    AWS, read it on their docsAzure, read it on their docsGCP, read it on their docsSAP(not on this cloud)
  14. AI Runtime 1xH100 accelerator (Beta)

    Supports 1xH100 accelerator, see Hardware options for details.

    #Machine Learning
    AWS, read it on their docsAzure, read it on their docsGCP(not on this cloud)SAP(not on this cloud)
  15. AI Runtime is now in Public Preview

    Adds GPU support to serverless compute for deep learning workloads.

    #Machine Learning
    AWS, read it on their docsAzure, read it on their docsGCP(not on this cloud)SAP(not on this cloud)
  16. Declarative Feature Engineering APIs (Beta)

    Define time-windowed aggregation features from data sources, materialize to Delta tables or Lakebase. Available in us-east-1 and us-west-2 regions.

    #Machine Learning
    AWS, read it on their docsAzure(not on this cloud)GCP(not on this cloud)SAP(not on this cloud)
  17. Endpoint telemetry for custom model serving endpoints (Beta)

    Stores logs, traces, and metrics in Unity Catalog Delta tables using OpenTelemetry.

    #Machine Learning#Model Serving
    AWS, read it on their docsAzure, read it on their docsGCP(not on this cloud)SAP(not on this cloud)
  18. View and restore your recent workspace tab sessions

    Restores up to 10 recent sessions.

    #Lakeflow#Notebooks#Workspace
    AWS, read it on their docsAzure, read it on their docsGCP, read it on their docsSAP(unknown)
  19. Serverless workspaces are now generally available

    Come pre-configured with serverless compute and default storage.

    #Serverless#Workspace
    AWS, read it on their docsAzure, read it on their docsGCP, read it on their docsSAP(unknown)
  20. Get this table by email. One Monday mail covering the week, filtered to the clouds and products you run. Weekly digest
  21. Store and query MLflow traces in Unity Catalog (Beta)

    Stores MLflow traces in Delta tables for long-term retention and analysis using OpenTelemetry format.

    #Gen AI#MLflow
    AWS, read it on their docsAzure, read it on their docsGCP(not on this cloud)SAP(unknown)

Headlines, dates and product areas are Databricks' own, from the release notes published for each cloud, and every item links to the note it came from. The one-line summaries are ours. Not a Databricks product and not affiliated with Databricks, Inc.