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9 releases of 1321

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  1. 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)
  2. Databricks Runtime 19 and Databricks Runtime 19 for Machine Learning are in Beta

    Powered by Apache Spark 4.2.0.

    #Databricks Runtime
    AWS, read it on their docsAzure, read it on their docsGCP, read it on their docsSAP(not on this cloud)
  3. Databricks Runtime 18 and Databricks Runtime 18 for Machine Learning are in Beta

    Powered by Apache Spark 4.1.0, replacing separate release notes for feature versions.

    #Databricks Runtime
    AWS, read it on their docsAzure, read it on their docsGCP, read it on their docsSAP(not on this cloud)
  4. 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)
  5. Databricks Runtime 18.2 and Databricks Runtime 18.2 ML are in Beta

    Powered by Apache Spark 4.1.0.

    #Databricks Runtime
    AWS, read it on their docsAzure, read it on their docsGCP, read it on their docsSAP(not on this cloud)
  6. Databricks Runtime 18.1 and Databricks Runtime 18.1 ML are in Beta

    Powered by Apache Spark 4.1.0.

    #Databricks Runtime
    AWS, read it on their docsAzure, read it on their docsGCP, read it on their docsSAP(unknown)
  7. Run the change, do not just read it. Hands-on labs in your own Databricks workspace, graded when you submit. Browse labs
  8. Databricks Runtime 18.0 and Databricks Runtime 18.0 ML are in Beta

    Powered by Apache Spark 4.1.0, includes JDK 21 as default.

    #Databricks Runtime
    AWS, read it on their docsAzure, read it on their docsGCP, read it on their docsSAP(unknown)
  9. Databricks Runtime 17.3 LTS and Databricks Runtime 17.3 LTS ML are in Beta

    Powered by Apache Spark 4.0.0, includes new configuration options.

    #Databricks Runtime
    AWS, read it on their docsAzure, read it on their docsGCP, read it on their docsSAP(unknown)
  10. Admins can now manage a workspace's serverless base environments (Public Preview)

    Defines custom environment specs for serverless notebooks, allows setting a default for new notebooks.

    #Notebooks#Serverless
    AWS, read it on their docsAzure, read it on their docsGCP, read it on their docsSAP(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.