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Serverless Prediction of a Product Feedback (#AzureDevStories)

This Project is created as a part of Azure Dev Stories Challenge and won the First Prize 🏆

I've published a detailed article about this project in the Dev Community

Architecture Diagram

Components Used

  • Yammer
  • LUIS
  • Logic Apps
  • AzureDevOps WorkItems
  • Cosmos DB
  • Signal R
  • Azure Functions

Yammer

Users provide their feedback about the product. It could be many, for the demo purpose I just choose 2 topics (Bug, Feature)

LUIS

Creating Intents for Bugs and Feedbacks in the LUIS.

Logic Apps

Predicting the Intents i.e, Bug or Feedback based on the Yammer Post by the user and take necessary actions

AzureDevOps WorkItems

Create Bug/Feature if the top intent of the Post matched with LUIS

Cosmos DB

Insert the document in Cosmos DB if the top intent of the Post is None

Signal R

Serverless Signal R used to autorefresh the WebPage for the Live updates of the None intent

AzureFunction

Azure Function Integrated with Cosmos Change feed and provide live Updates in the static Web Page with the help of Signal R.

Static WebPage with Live CosmosDB Feed for None intent which helps the developers to take necessary action like re-train the LUIS model with additional utterances or create new intents/entites