Building the solution

In the previous section, we introduced our use case, which is to submit company registrations for public trading to the SEC, 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 get right down to business and start executing the tasks that will build our solution. But first, there are some prerequisites we have to take care of.

Setting up for the solution build

If you have not done so in the previous chapters, you will have to create an Amazon SageMaker Jupyter notebook, as well as setting 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, and provide the bucket...