Book Image

Deep Learning for Natural Language Processing

By : Karthiek Reddy Bokka, Shubhangi Hora, Tanuj Jain, Monicah Wambugu
Book Image

Deep Learning for Natural Language Processing

By: Karthiek Reddy Bokka, Shubhangi Hora, Tanuj Jain, Monicah Wambugu

Overview of this book

Applying deep learning approaches to various NLP tasks can take your computational algorithms to a completely new level in terms of speed and accuracy. Deep Learning for Natural Language Processing starts by highlighting the basic building blocks of the natural language processing domain. The book goes on to introduce the problems that you can solve using state-of-the-art neural network models. After this, delving into the various neural network architectures and their specific areas of application will help you to understand how to select the best model to suit your needs. As you advance through this deep learning book, you’ll study convolutional, recurrent, and recursive neural networks, in addition to covering long short-term memory networks (LSTM). Understanding these networks will help you to implement their models using Keras. In later chapters, you will be able to develop a trigger word detection application using NLP techniques such as attention model and beam search. By the end of this book, you will not only have sound knowledge of natural language processing, but also be able to select the best text preprocessing and neural network models to solve a number of NLP issues.
Table of Contents (11 chapters)

Deployment

Up to this point, the data science team has a Flask web service that works on a local system. However, the web development team is still not in a position to use the service, since it only runs on a local system. So, we need to host this web service somewhere on a cloud platform so that it is also available for the web development team to use. This section provides a basic pipeline for the deployment to work, which can be broken down into the following steps:

  1. Make changes to the Flask web app so that it can be deployed.
  2. Use Docker to wrap the flask web application into a container.
  3. Host the container on an Amazon Web Services (AWS) EC2 instance.

Let's look at each of these steps in detail.

Making Changes to a Flask Web App

The flask application that was coded in the FLASK section ran on a local web address: http://127.0.0.1:5000. Since our intention is to host it on the internet, this address needs to be changed to: 0.0.0.0. Additionally, since the default HTTP...