Summary
We just completed the journey to build an NLP model in Spark NLP and track it in Comet!
Throughout this chapter, we described some general concepts regarding NLP, including the basic NLP workflow, how you can classify NLP tools, and the main NLP challenges. In addition, you have seen the main structure of the Spark NLP package and how to set up the environment to make it work. We also illustrated some important concepts, such as annotators and pipelines.
In the last part of the chapter, you implemented a practical use case that showed you how to track an NLP experiment in Comet, as well as how to build a report with the results of the experiment.
In the next chapter, we will review the basic concepts related to deep learning and how to perform it in Comet.
