Get familiar with all platform and email notifications about AI Models’ status
Updated
The article covers all the platform and email notifications about failures and completion of actions in AI Studio.
Sprinklr’s AI Studio allows you to test the AI models to your satisfaction and provide feedback to customize the models as per your requirements. Whenever you create an AI Project or Model, Sprinklr notifies you about its status – processed, failed, completed, etc. via a platform notification and email notification. By receiving an email notification, you do not need to access the platform to check the project status.
List of platform/email notifications
Notification | Trigger | Description |
Project <Project Name> is successfully processed | When the AI project is successfully processed | Whenever you create an AI Project, it goes for processing. And once it is processed successfully, you receive this notification. You can now start classifying text messages. |
Project <Project Name>processing failed. Recreate or update your project | When the AI Project processing fails | Whenever you create an AI Project, it goes for processing. And if the project processing fails due to any reason, you receive this notification. You need to recreate your project or update it with the correct information. |
Model <Model Name> training is successfully completed | When the AI Model training is successfully completed | After classifying the messages, the AI Model goes for training. And once the model is successfully trained, you receive this notification. This notification also means that the model is now ready for review, i.e. the validator can start reviewing the sample predictions. |
Model <Model Name> training failed. Retrain your project | When the AI Model training fails | After classifying the messages, the AI Model goes for training. And if the model training fails due to any reason, you receive this notification. You must reclassify your text messages before sending your AI Project again for the training. |
Model <Model Name> deployment successfully completed | When the AI Model is successfully deployed | After reviewing and approving sample predictions, you can send your AI Model for deployment. And once the AI Model is successfully deployed, you receive this notification. Listening dashboards can help you gain insights from AI model enrichments. So, create a Listening dashboard. |
Model <Model Name> deployment failed. Please re-deploy | When the AI Model deployment fails | After reviewing and approving sample predictions, you can send your AI Model for deployment. And if the AI Model deployment fails due to any reason, you receive this notification. You have to re-deploy your AI Project. |
Undeployment for AI Model <Model Name> has been completed successfully | When the AI model is successfully undeployed | This notification means that the AI Model you sent for undeployment has been successfully undeployed. To continue predicting using the AI Model, you can trigger deployment. |
Undeployment for AI Model <Model Name> has failed. Please try again or contact support | When the AI Model undeployment fails | When an AI Model fails during the process of undeployment, you receive this notification. In such a situation, you should re-trigger undeployment or you can simply contact the support team to look into the matter. |
Accuracy calculation for Project <Project Name> completed successfully | When the AI Model accuracy calculation is successfully completed | Accuracies are calculated with respect to Golden Dataset. You receive the above notification once the accuracy calculation is successfully completed. You start using the model now by deploying it. |
Accuracy calculation for Project <Project Name> failed. Please recalculate accuracy | When the AI Model accuracy calculation fails | This notification means that the accuracy calculation for your AI Project has failed. Make sure you have input the right values to Golden Dataset you are calculating the accuracy with. You can re-trigger accuracy calculation. |
Delete Project: snackbar for deletion to be queued: Project <Project Name> has been queued for deletion | When the model is queued for deletion | Whenever you delete an AI Project, it does not get instantly deleted. Instead, it goes for the deletion process, and you receive the above notification. There could be hundreds of AI Projects being queued for deletion, and they get deleted one by one. |
Project <Project Name> validation completed. | When all the messages are validated | This notification is sent when all the messages have been validated and the project is ready for further steps. |
Project <Project Name> <No. Of days> days left to validate messages. | Notitification is sent stating the remaining time avaialble for validation |
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Project <Project Name> validation approved | When a project is approved | This notification is sent when the project validation has been approved by tge admin |
AI Model <Model Name> has been discarded | When a trained model is discarded | This notification means that the trained project has been discarded by the user from the "Accept & Deploy / Discard" screen |
You will receive these in-platform notifications only if –
You have the permission for the required action
The project is shared with you or you have the access permission
You have subscribed to the AI Studio notifications
You created the project or the project has been shared with you
Note: The aforementioned notifications are also triggered as an email sent to the relevant users.