AWS neptune documentation change

Service: neptune · 2026-04-10 · Documentation low

File: neptune/latest/userguide/machine-learning-on-graphs-inference-endpoint.md

Summary

Updated documentation to provide multiple implementation examples (AWS CLI, SDK, awscurl, and curl) for creating ML inference endpoints, replacing a single curl example with comprehensive code samples and clarifying endpoint URL format.

Security assessment

The changes are purely documentation improvements showing different ways to call the same API. No security vulnerabilities, patches, or security features are mentioned. The update standardizes placeholder formatting (changing '(your Neptune endpoint)' to 'your-neptune-endpoint:port') and adds examples for various SDKs/tools, which is routine documentation maintenance.

Diff

diff --git a/neptune/latest/userguide/machine-learning-on-graphs-inference-endpoint.md b/neptune/latest/userguide/machine-learning-on-graphs-inference-endpoint.md
index e3f549e6c..86b6f0e06 100644
--- a//neptune/latest/userguide/machine-learning-on-graphs-inference-endpoint.md
+++ b//neptune/latest/userguide/machine-learning-on-graphs-inference-endpoint.md
@@ -13 +13,53 @@ An inference endpoint lets you query one specific model that the model-training
-After you have completed model training on data that you exported from Neptune, you can create an inference endpoint using a `curl` (or `awscurl`) command like the following:
+After you have completed model training on data that you exported from Neptune, you can create an inference endpoint using a command like the following:
+
+AWS CLI
+    
+    
+    
+    aws neptunedata create-ml-endpoint \
+      --endpoint-url https://your-neptune-endpoint:port \
+      --id "(a unique ID for the new endpoint)" \
+      --ml-model-training-job-id "(the model-training job-id of a completed job)"
+
+For more information, see [create-ml-endpoint](https://docs.aws.amazon.com/cli/latest/reference/neptunedata/create-ml-endpoint.html) in the AWS CLI Command Reference.
+
+SDK
+    
+    
+    
+    import boto3
+    from botocore.config import Config
+    
+    client = boto3.client(
+        'neptunedata',
+        endpoint_url='https://your-neptune-endpoint:port',
+        config=Config(read_timeout=None, retries={'total_max_attempts': 1})
+    )
+    
+    response = client.create_ml_endpoint(
+        id='(a unique ID for the new endpoint)',
+        mlModelTrainingJobId='(the model-training job-id of a completed job)'
+    )
+    
+    print(response)
+
+awscurl
+    
+    
+    
+    awscurl https://your-neptune-endpoint:port/ml/endpoints \
+      --region us-east-1 \
+      --service neptune-db \
+      -X POST \
+      -H 'Content-Type: application/json' \
+      -d '{
+            "id" : "(a unique ID for the new endpoint)",
+            "mlModelTrainingJobId": "(the model-training job-id of a completed job)"
+          }'
+
+###### Note
+
+This example assumes that your AWS credentials are configured in your environment. Replace `us-east-1` with the Region of your Neptune cluster.
+
+curl
+    
@@ -17 +69 @@ After you have completed model training on data that you exported from Neptune,
-      -X POST https://(your Neptune endpoint)/ml/endpoints
+      -X POST https://your-neptune-endpoint:port/ml/endpoints \
@@ -25,0 +78,52 @@ You can also create an inference endpoint from a model created by a completed mo
+AWS CLI
+    
+    
+    
+    aws neptunedata create-ml-endpoint \
+      --endpoint-url https://your-neptune-endpoint:port \
+      --id "(a unique ID for the new endpoint)" \
+      --ml-model-transform-job-id "(the model-transform job-id of a completed job)"
+
+For more information, see [create-ml-endpoint](https://docs.aws.amazon.com/cli/latest/reference/neptunedata/create-ml-endpoint.html) in the AWS CLI Command Reference.
+
+SDK
+    
+    
+    
+    import boto3
+    from botocore.config import Config
+    
+    client = boto3.client(
+        'neptunedata',
+        endpoint_url='https://your-neptune-endpoint:port',
+        config=Config(read_timeout=None, retries={'total_max_attempts': 1})
+    )
+    
+    response = client.create_ml_endpoint(
+        id='(a unique ID for the new endpoint)',
+        mlModelTransformJobId='(the model-transform job-id of a completed job)'
+    )
+    
+    print(response)
+
+awscurl
+    
+    
+    
+    awscurl https://your-neptune-endpoint:port/ml/endpoints \
+      --region us-east-1 \
+      --service neptune-db \
+      -X POST \
+      -H 'Content-Type: application/json' \
+      -d '{
+            "id" : "(a unique ID for the new endpoint)",
+            "mlModelTransformJobId": "(the model-transform job-id of a completed job)"
+          }'
+
+###### Note
+
+This example assumes that your AWS credentials are configured in your environment. Replace `us-east-1` with the Region of your Neptune cluster.
+
+curl
+    
+    
@@ -28 +132 @@ You can also create an inference endpoint from a model created by a completed mo
-      -X POST https://(your Neptune endpoint)/ml/endpoints
+      -X POST https://your-neptune-endpoint:port/ml/endpoints \