Update how-to-troubleshoot-online-endpoints.md

Update 429s and ImageBuildFailure

Author: aishanibhalla

articles/machine-learning/how-to-troubleshoot-online-endpoints.md

@@ -213,13 +213,15 @@ If you are creating or updating a Kubernetes online deployment, you can see [Com
 
 ### ERROR: ImageBuildFailure
 
-This error is returned when the environment (docker image) is being built. You can check the build log for more information on the failure(s). The build log is located in the default storage for your Azure Machine Learning workspace. The exact location may be returned as part of the error. For example, "The build log is available in the workspace blob store '[storage-account-name]' under the path '/azureml/ImageLogs/your-image-id/build.log'". In this case, "azureml" is the name of the blob container in the storage account.
+This error is returned when the environment (docker image) is being built. You can check the build log for more information on the failure(s). The build log is located in the default storage for your Azure Machine Learning workspace. The exact location may be returned as part of the error. For example, `"The build log is available in the workspace blob store '[storage-account-name]' under the path '/azureml/ImageLogs/your-image-id/build.log'"`. In this case, "azureml" is the name of the blob container in the storage account.
 
 This is a list of common image build failure scenarios:
 
 * [Azure Container Registry (ACR) authorization failure](#container-registry-authorization-failure)
 * [Generic or unknown failure](#generic-image-build-failure)
 
+We also recommend reviewing the default [probe settings](reference-yaml-deployment-managed-online.md#probesettings) in case of ImageBuild timeouts. 
+
 #### Container registry authorization failure
 
 If the error message mentions `"container registry authorization failure"` that means you cannot access the container registry with the current credentials.
@@ -675,7 +677,7 @@ These are common error codes when consuming managed online endpoints with REST r
 | 404         | Not found                 | The endpoint doesn't have any valid deployment with positive weight.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
 | 408         | Request timeout           | The model execution took longer than the timeout supplied in `request_timeout_ms` under `request_settings` of your model deployment config.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
 | 424         | Model Error               | If your model container returns a non-200 response, Azure returns a 424. Check the `Model Status Code` dimension under the `Requests Per Minute` metric on your endpoint's [Azure Monitor Metric Explorer](../azure-monitor/essentials/metrics-getting-started.md). Or check response headers `ms-azureml-model-error-statuscode` and `ms-azureml-model-error-reason` for more information. If 424 comes with liveness or readiness probe failing, consider adjusting [probe settings](reference-yaml-deployment-managed-online.md#probesettings) to allow longer time to probe liveness or readiness of the container. |
-| 429         | Too many pending requests | Your model is getting more requests than it can handle. Azure Machine Learning allows maximum 2 * `max_concurrent_requests_per_instance` * `instance_count` requests in parallel at any time and rejects extra requests. You can confirm these settings in your model deployment config under `request_settings` and `scale_settings`, respectively. If you're using auto-scaling, this error means that your model is getting requests faster than the system can scale up. With auto-scaling, you can try to resend requests with [exponential backoff](https://en.wikipedia.org/wiki/Exponential_backoff). Doing so can give the system time to adjust. Apart from enabling auto-scaling, you could also increase the number of instances by using the [code to calculate instance count](#how-to-calculate-instance-count). |
+| 429         | Too many pending requests | Your model is currently getting more requests than it can handle. Azure Machine Learning has implemented a system that permits a maximum of `2 * max_concurrent_requests_per_instance * instance_count requests` to be processed in parallel at any given moment to guarantee smooth operation. Additional requests that exceed this maximum will be rejected. You can review your model deployment configuration under the request_settings and scale_settings sections to verify and adjust these settings. Additionally, as outlined in the [YAML definition for RequestSettings](reference-yaml-deployment-managed-online.md#requestsettings), it is important to ensure that the environment variable `WORKER_COUNT` is correctly passed. <br><br> If you're using auto-scaling and get this error, it means your model is getting requests quicker than the system can scale up. In this situation, consider resending requests with an [exponential backoff](https://en.wikipedia.org/wiki/Exponential_backoff) to give the system the time it needs to adjust. You could also increase the number of instances by using [code to calculate instance count](#how-to-calculate-instance-count). These steps, combined with setting auto-scaling, will help ensure that your model is ready to handle the influx of requests. |
 | 429         | Rate-limiting             | The number of requests per second reached the [limit](./how-to-manage-quotas.md#azure-machine-learning-managed-online-endpoints) of managed online endpoints.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
 | 500         | Internal server error     | Azure Machine Learning-provisioned infrastructure is failing.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |