This chapter was a brief introduction to how to build an en- to-end system that trains a sentiment analysis model using Keras and deploys it in JavaScript using TensorFlow.js. The process of deploying the model in production is pretty seamless.
A potential next step would be to modify the JavaScript to predict sentiment as soon as a word is typed. As we mentioned previously, by deploying the model using TensorFlow.js, you can enable low- latency applications like real-time sentiment prediction without having to interact with the server.
Finally, we built a neural network in Python and deployed it in JavaScript. However, you can try building the entire model in JavaScript using TensorFlow.js.
In the next chapter, we will learn about Google's new library, TensorFlow Lite.