Building an NLP solution to improve customer service
In the previous section, we introduced the contact center use case for customer service, covered the architecture of the solution we will be building, and briefly walked through the solution components and workflow steps. In this section, we will start executing the tasks to build our solution. But first, there are some prerequisites that we must take care of.
Setting up to solve the use case
If you have not done so already in the previous chapters, you will have to create an Amazon SageMaker Jupyter notebook, and then set up Identity and Access Management (IAM) permissions for that notebook role to access the AWS services we will use in this notebook. After that, you will need to clone this book's GitHub repository (https://github.com/PacktPublishing/Natural-Language-Processing-with-AWS-AI-Services), create an Amazon S3 (https://aws.amazon.com/s3/) bucket, go to the Chapter 06 folder, open the chapter6-nlp-in-customer...
