Deploying and Using a Sentiment Analysis Model

You've learned how to create and train models in SageMaker and how you can deploy them. In this example we are going to look at how we can make use of a deployed model in a simple web app.

In order for our simple web app to interact with the deployed model we are going to have to solve a couple problems.

The first obstacle is something that has been mentioned earlier.

The endpoint that is created when we deploy a model using SageMaker is secured, meaning that only entities that are authenticated with AWS can send or receive data from the deployed model. This is a problem since authenticating for the purposes of a simple web app is a bit more work than we'd like.

So we will need to find a way to work around this.

The second obstacle is that our deployed model expects us to send it a review after it has been processed. That is, it assumes we have already tokenized the review and then created a bag of words encoding. However, we want our user to be able to type any review into our web app.

We will also see how we can overcome this.

To solve these issues we are going to need to use some additional Amazon services. In particular, we are going to look at Amazon Lambda and API Gateway.

In the mean time, I would encourage you to take a look at the IMDB Sentiment Analysis - XGBoost - Web App.ipynb notebook in the Tutorials folder. In the coming videos we will go through this notebook in detail, however, each of the steps involved is pretty well documented in the notebook itself.