update subtitle to include shadow, remove preview

Author: shohei1029

articles/machine-learning/how-to-safely-rollout-online-endpoints.md

@@ -318,10 +318,10 @@ Though `green` has 0% of traffic allocated, you can still invoke the endpoint an
 
 ---
 
-## Test the deployment with mirrored traffic (preview)
+## Shadow test the deployment with mirrored traffic
 [!INCLUDE [preview disclaimer](../../includes/machine-learning-preview-generic-disclaimer.md)]
 
-Once you've tested your `green` deployment, you can copy (or 'mirror') a percentage of the live traffic to it. Mirroring traffic doesn't change results returned to clients. Requests still flow 100% to the `blue` deployment. The mirrored percentage of the traffic is copied and submitted to the `green` deployment so you can gather metrics and logging without impacting your clients. Mirroring is useful when you want to validate a new deployment without impacting clients. For example, to check if latency is within acceptable bounds and that there are no HTTP errors.
+Once you've tested your `green` deployment, you can copy (or 'mirror') a percentage of the live traffic to it. Mirroring traffic doesn't change results returned to clients. Requests still flow 100% to the `blue` deployment. The mirrored percentage of the traffic is copied and submitted to the `green` deployment so you can gather metrics and logging without impacting your clients. Mirroring is useful when you want to validate a new deployment without impacting clients. For example, to check if latency is within acceptable bounds and that there are no HTTP errors. This kind of testing scenario is also known as shadow testing.
 
 > [!WARNING]
 > Mirroring traffic uses your [endpoint bandwidth quota](how-to-manage-quotas.md#azure-machine-learning-managed-online-endpoints) (default 5 MBPS). Your endpoint bandwidth will be throttled if you exceed the allocated quota. For information on monitoring bandwidth throttling, see [Monitor managed online endpoints](how-to-monitor-online-endpoints.md#metrics-at-endpoint-scope).
@@ -463,4 +463,4 @@ If you aren't going use the deployment, you should delete it with:
 - [View costs for an Azure Machine Learning managed online endpoint](how-to-view-online-endpoints-costs.md)
 - [Managed online endpoints SKU list](reference-managed-online-endpoints-vm-sku-list.md)
 - [Troubleshooting  online endpoints deployment and scoring](how-to-troubleshoot-managed-online-endpoints.md)
-- [Online endpoint YAML reference](reference-yaml-endpoint-online.md)
+- [Online endpoint YAML reference](reference-yaml-endpoint-online.md)