Merging v1.4.106 release

Author: aws-sdk-kotlin-ci

CHANGELOG.md

@@ -1,5 +1,24 @@
 # Changelog
 
+## [1.4.106] - 06/17/2025
+
+### Features
+* (**accessanalyzer**) We are launching a new analyzer type, internal access analyzer. The new analyzer will generate internal access findings, which help customers understand who within their AWS organization or AWS Account has access to their critical AWS resources.
+* (**acm**) Adds support for Exportable Public Certificates
+* (**backup**) AWS Backup is adding support for integration of its logically air-gapped vaults with the AWS Organizations Multi-party approval capability.
+* (**databasemigrationservice**) Add "Virtual" field to Data Provider as well as "S3Path" and "S3AccessRoleArn" fields to DataProvider settings
+* (**guardduty**) Adding support for extended threat detection for EKS Audit Logs and EKS Runtime Monitoring.
+* (**inspector2**) Add Code Repository Scanning as part of AWS InspectorV2
+* (**mpa**) This release enables customers to create Multi-party approval teams and approval requests to protect supported operations.
+* (**networkfirewall**) Release of Active Threat Defense in Network Firewall
+* (**organizations**) Add support for policy operations on the SECURITYHUB_POLICY policy type.
+* (**securityhub**) Adds operations, structures, and exceptions required for public preview release of Security Hub V2.
+* (**sts**) The AWS Security Token Service APIs AssumeRoleWithSAML and AssumeRoleWithWebIdentity can now be invoked without pre-configured AWS credentials in the SDK configuration.
+* (**wafv2**) AWS WAF can now suggest protection packs for you based on the application information you provide when you create a webACL.
+
+### Documentation
+* (**bedrock**) This release of the SDK has the API and documentation for the createcustommodel API. This feature lets you copy a trained model into Amazon Bedrock for inference.
+
 ## [1.4.105] - 06/16/2025
 
 ### Features

codegen/sdk/aws-models/accessanalyzer.json

@@ -1282,7 +1282,7 @@
                 }
             ],
             "traits": {
-                "smithy.api#documentation": "<p>Creates a new custom model in Amazon Bedrock from an existing SageMaker AI-trained Amazon Nova model stored in an Amazon-managed Amazon S3 bucket. After the model is active, you can use it for inference.</p> <p>To use the model for inference, you must purchase Provisioned Throughput for it. You can't use On-demand inference with these custom models. For more information about Provisioned Throughput, see <a href=\"https://docs.aws.amazon.com/bedrock/latest/userguide/prov-throughput.html\">Provisioned Throughput</a>.</p> <p>The model appears in <code>ListCustomModels</code> with a <code>customizationType</code> of <code>imported</code>. To track the status of the new model, you use the <code>GetCustomModel</code> API operation. The model can be in the following states:</p> <ul> <li> <p> <code>Creating</code> - Initial state during validation and registration</p> </li> <li> <p> <code>Active</code> - Model is ready for use in inference</p> </li> <li> <p> <code>Failed</code> - Creation process encountered an error</p> </li> </ul> <p>For more information about creating custom models, including specific model requirements, see <a href=\"https://docs.aws.amazon.com/bedrock/latest/userguide/create-custom-model-from-existing.html\">Import a SageMaker AI-trained Amazon Nova model</a> in the Amazon Bedrock User Guide. </p> <note> <p>You use the <code>CreateCustomModel</code> API to import only SageMaker AI-trained Amazon Nova models. To import open-source models, you use the <a href=\"https://docs.aws.amazon.com/bedrock/latest/APIReference/API_CreateModelImportJob.html\">CreateModelImportJob</a>. </p> </note> <p> <b>Related APIs</b> </p> <ul> <li> <p> <a href=\"https://docs.aws.amazon.com/bedrock/latest/APIReference/API_GetCustomModel.html\">GetCustomModel</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/bedrock/latest/APIReference/API_ListCustomModels.html\">ListCustomModels</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/bedrock/latest/APIReference/API_DeleteCustomModel.html\">DeleteCustomModel</a> </p> </li> </ul>",
+                "smithy.api#documentation": "<p>Creates a new custom model in Amazon Bedrock. After the model is active, you can use it for inference.</p> <p>To use the model for inference, you must purchase Provisioned Throughput for it. You can't use On-demand inference with these custom models. For more information about Provisioned Throughput, see <a href=\"https://docs.aws.amazon.com/bedrock/latest/userguide/prov-throughput.html\">Provisioned Throughput</a>.</p> <p>The model appears in <code>ListCustomModels</code> with a <code>customizationType</code> of <code>imported</code>. To track the status of the new model, you use the <code>GetCustomModel</code> API operation. The model can be in the following states:</p> <ul> <li> <p> <code>Creating</code> - Initial state during validation and registration</p> </li> <li> <p> <code>Active</code> - Model is ready for use in inference</p> </li> <li> <p> <code>Failed</code> - Creation process encountered an error</p> </li> </ul> <p> <b>Related APIs</b> </p> <ul> <li> <p> <a href=\"https://docs.aws.amazon.com/bedrock/latest/APIReference/API_GetCustomModel.html\">GetCustomModel</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/bedrock/latest/APIReference/API_ListCustomModels.html\">ListCustomModels</a> </p> </li> <li> <p> <a href=\"https://docs.aws.amazon.com/bedrock/latest/APIReference/API_DeleteCustomModel.html\">DeleteCustomModel</a> </p> </li> </ul>",
                 "smithy.api#examples": [
                     {
                         "title": "Successful CreateCustomModel API call",
@@ -1332,7 +1332,7 @@
                 "modelSourceConfig": {
                     "target": "com.amazonaws.bedrock#ModelDataSource",
                     "traits": {
-                        "smithy.api#documentation": "<p>The data source for the model. The Amazon S3 URI in the model source must be for the Amazon-managed Amazon S3 bucket containing your model artifacts. SageMaker AI creates this bucket when you run your first SageMaker AI training job.</p>",
+                        "smithy.api#documentation": "<p>The data source for the model. The Amazon S3 URI in the model source must be for the Amazon-managed Amazon S3 bucket containing your model artifacts.</p>",
                         "smithy.api#required": {}
                     }
                 },
@@ -12910,7 +12910,7 @@
                 }
             },
             "traits": {
-                "smithy.api#documentation": "<p>The Amazon S3 data source of the model to import. For the <a href=\"https://docs.aws.amazon.com/bedrock/latest/APIReference/API_CreateCustomModel.html\">CreateCustomModel</a> API operation, you must specify the Amazon S3 URI for the Amazon-managed Amazon S3 bucket containing your model artifacts. SageMaker AI creates this bucket when you run your first SageMaker AI training job.</p>"
+                "smithy.api#documentation": "<p>The Amazon S3 data source of the model to import. </p>"
             }
         },
         "com.amazonaws.bedrock#S3InputFormat": {