Chapter 13. Using Text Analytics in Production

We have introduced several blueprints so far and understood their application to multiple use cases. Any analysis or machine learning model is most valuable when it can be used easily by others. In this chapter, we will provide blueprints that will allow you to share the text classifier from one of our earlier chapters and also deploy to a cloud environment allowing anybody to make use of what we’ve built.

Consider that you used one of the blueprints in Chapter 10 in this book to analyze various car models using data from Reddit. If one of your colleagues was interested in doing the same analysis for the motorcycle industry, it should be simple to change the data source and reuse the code. In practice, this can prove much more difficult because your colleague will first have to set up an environment similar to the one you used by installing the same version of Python and all the required packages. It’s possible that they might be working on a different operating system where the installation steps are different. Or consider that the clients to whom you presented the analysis are extremely happy and come back three months later asking you to cover many more industries. Now you have to repeat the same analysis but ensure that the code and environment remain the same. The volume of data for this analysis could be much larger, and your system resources may not be sufficient enough, prompting a move to use cloud computing resources. You ...

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