AWS sagemaker documentation change

Service: sagemaker · 2026-04-22 · Documentation low

File: sagemaker/latest/dg/your-algorithms-inference-code.md

Summary

Added detailed guidance on implementing meaningful health checks for the /ping endpoint, emphasizing verification of model readiness, resources, and inference functionality to prevent routing requests to unhealthy instances.

Security assessment

The change encourages better health check implementations to improve availability and reduce errors, but does not address a specific security vulnerability or weakness; it is a best practice for reliability.

Diff

diff --git a/sagemaker/latest/dg/your-algorithms-inference-code.md b/sagemaker/latest/dg/your-algorithms-inference-code.md
index e723815a6..18849bc51 100644
--- a//sagemaker/latest/dg/your-algorithms-inference-code.md
+++ b//sagemaker/latest/dg/your-algorithms-inference-code.md
@@ -401 +401,16 @@ If the container does not begin to pass health checks by consistently responding
-While the minimum bar is for the container to return a static 200, a container developer can use this functionality to perform deeper checks. The request timeout on `/ping` attempts is 2 seconds.
+While the minimum bar is for the container to return a static 200, a container developer can use this functionality to perform deeper checks. For example, the container can verify that the model is loaded into memory and can serve inference requests. The request timeout on `/ping` attempts is 2 seconds.
+
+We strongly recommend implementing meaningful health checks rather than returning a static 200. The `/ping` endpoint is the main signal SageMaker AI uses to determine whether an instance is healthy. If your container always returns 200 — even when the model has failed to load, run out of memory, or entered a bad state — SageMaker AI continues routing inference requests to that instance. This results in sustained invocation errors for your application until the instance is manually replaced or the endpoint is updated.
+
+A well-implemented `/ping` handler should verify that:
+
+  * The model artifact is loaded and ready to serve
+
+  * Critical resources (memory, disk, GPU if applicable) are available
+
+  * The inference code path is functional (for example, a lightweight test prediction succeeds)
+
+
+
+
+When `/ping` correctly reports an unhealthy state by returning a non-200 response, SageMaker AI detects the failure and automatically replaces the instance (excluding endpoints that use inference components), minimizing downtime for your application.