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.

Author: aws-sdk-dotnet-automation

generator/ServiceModels/bedrock/bedrock-2023-04-20.docs.json

@@ -3,7 +3,7 @@
   "service": "<p>Describes the API operations for creating, managing, fine-turning, and evaluating Amazon Bedrock models.</p>",
   "operations": {
     "BatchDeleteEvaluationJob": "<p>Deletes a batch of evaluation jobs. An evaluation job can only be deleted if it has following status <code>FAILED</code>, <code>COMPLETED</code>, and <code>STOPPED</code>. You can request up to 25 model evaluation jobs be deleted in a single request.</p>",
-    "CreateCustomModel": "<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>",
+    "CreateCustomModel": "<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>",
     "CreateEvaluationJob": "<p>Creates an evaluation job.</p>",
     "CreateGuardrail": "<p>Creates a guardrail to block topics and to implement safeguards for your generative AI applications.</p> <p>You can configure the following policies in a guardrail to avoid undesirable and harmful content, filter out denied topics and words, and remove sensitive information for privacy protection.</p> <ul> <li> <p> <b>Content filters</b> - Adjust filter strengths to block input prompts or model responses containing harmful content.</p> </li> <li> <p> <b>Denied topics</b> - Define a set of topics that are undesirable in the context of your application. These topics will be blocked if detected in user queries or model responses.</p> </li> <li> <p> <b>Word filters</b> - Configure filters to block undesirable words, phrases, and profanity. Such words can include offensive terms, competitor names etc.</p> </li> <li> <p> <b>Sensitive information filters</b> - Block or mask sensitive information such as personally identifiable information (PII) or custom regex in user inputs and model responses.</p> </li> </ul> <p>In addition to the above policies, you can also configure the messages to be returned to the user if a user input or model response is in violation of the policies defined in the guardrail.</p> <p>For more information, see <a href=\"https://docs.aws.amazon.com/bedrock/latest/userguide/guardrails.html\">Amazon Bedrock Guardrails</a> in the <i>Amazon Bedrock User Guide</i>.</p>",
     "CreateGuardrailVersion": "<p>Creates a version of the guardrail. Use this API to create a snapshot of the guardrail when you are satisfied with a configuration, or to compare the configuration with another version.</p>",
@@ -2496,7 +2496,7 @@
     "ModelDataSource": {
       "base": "<p>The data source of the model to import.</p>",
       "refs": {
-        "CreateCustomModelRequest$modelSourceConfig": "<p>The data source for the model. The Amazon S3 URI in the model source must be for the Amazon-managed Amazon S3 bucket containing your model artifacts. SageMaker AI creates this bucket when you run your first SageMaker AI training job.</p>",
+        "CreateCustomModelRequest$modelSourceConfig": "<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>",
         "CreateModelImportJobRequest$modelDataSource": "<p>The data source for the imported model.</p>",
         "GetImportedModelResponse$modelDataSource": "<p>The data source for this imported model.</p>",
         "GetModelImportJobResponse$modelDataSource": "<p>The data source for the imported model.</p>"
@@ -3099,7 +3099,7 @@
       }
     },
     "S3DataSource": {
-      "base": "<p>The Amazon S3 data source of the model to import. For the <a href=\"https://docs.aws.amazon.com/bedrock/latest/APIReference/API_CreateCustomModel.html\">CreateCustomModel</a> API operation, you must specify the Amazon S3 URI for the Amazon-managed Amazon S3 bucket containing your model artifacts. SageMaker AI creates this bucket when you run your first SageMaker AI training job.</p>",
+      "base": "<p>The Amazon S3 data source of the model to import. </p>",
       "refs": {
         "ModelDataSource$s3DataSource": "<p>The Amazon S3 data source of the model to import.</p>"
       }

generator/ServiceModels/bedrock/bedrock-2023-04-20.normal.json

@@ -51,7 +51,7 @@
         {"shape":"ServiceQuotaExceededException"},
         {"shape":"ThrottlingException"}
       ],
-      "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>"
+      "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>"
     },
     "CreateEvaluationJob":{
       "name":"CreateEvaluationJob",
@@ -1456,7 +1456,7 @@
         },
         "modelSourceConfig":{
           "shape":"ModelDataSource",
-          "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>"
+          "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>"
         },
         "modelKmsKeyArn":{
           "shape":"KmsKeyArn",
@@ -7773,7 +7773,7 @@
           "documentation":"<p>The URI of the Amazon S3 data source.</p>"
         }
       },
-      "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>"
+      "documentation":"<p>The Amazon S3 data source of the model to import. </p>"
     },
     "S3InputFormat":{
       "type":"string",

sdk/src/Services/Bedrock/Generated/Model/CreateCustomModelRequest.cs

@@ -31,9 +31,8 @@ namespace Amazon.Bedrock.Model
 {
     /// <summary>
     /// Container for the parameters to the CreateCustomModel operation.
-    /// 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.
+    /// Creates a new custom model in Amazon Bedrock. After the model is active, you can use
+    /// it for inference.
     /// 
     ///  
     /// <para>
@@ -62,18 +61,6 @@ namespace Amazon.Bedrock.Model
     /// </para>
     ///  </li> </ul> 
     /// <para>
-    /// 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. 
-    /// </para>
-    ///  <note> 
-    /// <para>
-    /// You use the <c>CreateCustomModel</c> 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>.
-    /// 
-    /// </para>
-    ///  </note> 
-    /// <para>
     ///  <b>Related APIs</b> 
     /// </para>
     ///  <ul> <li> 
@@ -174,8 +161,7 @@ internal bool IsSetModelName()
         /// Gets and sets the property ModelSourceConfig. 
         /// <para>
         /// 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.
+        /// Amazon-managed Amazon S3 bucket containing your model artifacts.
         /// </para>
         /// </summary>
         [AWSProperty(Required=true)]

sdk/src/Services/Bedrock/Generated/Model/S3DataSource.cs

@@ -30,10 +30,7 @@
 namespace Amazon.Bedrock.Model
 {
     /// <summary>
-    /// 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.
+    /// The Amazon S3 data source of the model to import.
     /// </summary>
     public partial class S3DataSource
     {

sdk/src/Services/Bedrock/Generated/_bcl/AmazonBedrockClient.cs

@@ -347,9 +347,8 @@ public virtual BatchDeleteEvaluationJobResponse BatchDeleteEvaluationJob(BatchDe
 
 
         /// <summary>
-        /// 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.
+        /// Creates a new custom model in Amazon Bedrock. After the model is active, you can use
+        /// it for inference.
         /// 
         ///  
         /// <para>
@@ -378,18 +377,6 @@ public virtual BatchDeleteEvaluationJobResponse BatchDeleteEvaluationJob(BatchDe
         /// </para>
         ///  </li> </ul> 
         /// <para>
-        /// 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. 
-        /// </para>
-        ///  <note> 
-        /// <para>
-        /// You use the <c>CreateCustomModel</c> 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>.
-        /// 
-        /// </para>
-        ///  </note> 
-        /// <para>
         ///  <b>Related APIs</b> 
         /// </para>
         ///  <ul> <li> 
@@ -451,9 +438,8 @@ public virtual CreateCustomModelResponse CreateCustomModel(CreateCustomModelRequ
 
 
         /// <summary>
-        /// 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.
+        /// Creates a new custom model in Amazon Bedrock. After the model is active, you can use
+        /// it for inference.
         /// 
         ///  
         /// <para>
@@ -482,18 +468,6 @@ public virtual CreateCustomModelResponse CreateCustomModel(CreateCustomModelRequ
         /// </para>
         ///  </li> </ul> 
         /// <para>
-        /// 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. 
-        /// </para>
-        ///  <note> 
-        /// <para>
-        /// You use the <c>CreateCustomModel</c> 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>.
-        /// 
-        /// </para>
-        ///  </note> 
-        /// <para>
         ///  <b>Related APIs</b> 
         /// </para>
         ///  <ul> <li>