Merge pull request #251889 from SimranArora904/patch-15

Update how-to-manage-quotas.md

Author: prmerger-automator[bot]

articles/machine-learning/how-to-manage-quotas.md

@@ -122,6 +122,11 @@ The following table shows more limits in the platform. Reach out to the Azure Ma
 
 <sup>2</sup> Jobs on a low-priority node can be preempted whenever there's a capacity constraint. We recommend that you implement checkpoints in your job.
 
+### Azure Machine Learning shared quota
+Azure Machine Learning provides a pool of shared quota that is available for different users across various regions to use concurrently. Depending upon availability, users can temporarily access quota from the shared pool, and use the quota to perform testing for a limited amount of time. The specific time duration depends on the use case. By temporarily using quota from the quota pool, you no longer need to file a support ticket for a short-term quota increase or wait for your quota request to be approved before you can proceed with your workload. 
+
+Use of the shared quota pool is available for running Spark jobs and for testing inferencing for Llama models from the Model Catalog. You should use the shared quota only for creating temporary test endpoints, not production endpoints. For endpoints in production, you should request dedicated quota by [filing a support ticket](https://ml.azure.com/quota). Billing for shared quota is usage-based, just like billing for dedicated virtual machine families.
+
 ### Azure Machine Learning managed online endpoints
 
 Azure Machine Learning managed online endpoints have limits described in the following table. These limits are _regional_, meaning that you can use up to these limits per each region you're using.