You have a large corpus of written support cases that can be classified into 3 separate categories: Technical Support, Billing Support, or Other Issues. You need to quickly build, test, and deploy a service that will automatically classify future written requests into one of the categories. How should you configure the pipeline?
To automatically classify future written support cases into the categories of Technical Support, Billing Support, or Other Issues, using AutoML Natural Language is the most appropriate approach. AutoML Natural Language allows for a quick build, test, and deployment process which aligns with the requirement to swiftly set up the service. Additionally, it provides an easy way to deploy the model as a REST API, facilitating seamless integration into existing systems.
ans: B A => no, you need customization. C, B => more work and complexity B => AutoML is easier and faster and "you need to quickly build, test, and deploy". Also the REST API part fits our use case.
B wish0035 explained
B will give quick result on classification
I think it's B because of the deployment
Quickly: AutoML: B.
ans B becouse es more fast
I think it's B, but I don't understand why it doesn't suggest to deploy the model on Vertex AI instead of as a REST API.
Went with B
"quickly build, test and deploy" + custom categories -> AutoML
AutoML is faster and offers the requisite REST API
Went with B
B) AutoML NLP