Service Request classification, questionnaire filling and call logging I am very new to machine learning. I just went through some of the tutorials in Azure and completed one practice workflow(car price prediction). I hope I can ask basic questions here.

Scenario : We get service request from our customers via email. This has fields like customer name, user name, email id, Equipment affected, type of call and Issue experienced(this is a free text area).

The employee reads this email, mainly the issue experienced. Based on the issue experienced section, s/he takes the appropriate actions. We will have 4-6 fields(type of request , a few questionnaire etc). The issue experienced is a free text area where customer can write anything about the issue.

Does this qualify as a AI model if we have last 2-3 years data. If yes, Is multi class classification the solution? If not, which ML algorithm needs to be used here. Can I rely on Azure for this or do we need to build a new model/algorithm for this?

Sorry if it is a too basic question
He reads this and in another application he logs a call. There he has to fill the request type and a number of questionnaires. 


This is a "multi class" classification problem. 

You can start with a library that makes it easy to mix text , categorical and numerical attributes. 

Text : "issue experienced" attribute   (Library should be able to apply NLP on text )
Categorical : "type of request" and similar attributes 
Numerical : "price" and similar attribute (if applicable) 

One such library is : Uber Lugwig 

If you have data in CSV, it should not take more than a few hours to train the model. 

Documentation : https://uber.github.io/ludwig/user_guide/ 

Introduction : https://hackaday.com/2019/02/25/ludwig-promises-easy-machine-learning-from-uber/