NLP workflow template

Some of us would love to work on Natural Language Processing for its sheer intellectual challenges – across research and engineering. To measure our progress, having a workflow with rough time estimates is really valuable. In this short section, we will briefly outline what a usual NLP or even most applied machine learning processes look like.

Most people I've learned from like to use a (roughly) five-step process:

• Understanding the problem
• Understanding and preparing data
• Quick wins: proof of concepts
• Iterating and improving the results
• Evaluation and deployment

This is just a process template. It has a lot of room for customization regarding the engineering culture in your company. Any of these steps can be broken down further. For instance, data preparation and understanding can be split further into analysis and cleaning. Similarly, the proof of concept step may involve multiple experiments, and a demo or a report submission of best results from those.

Although this appears to be a strictly linear process, it is not so. More often than not, you will want to revisit a previous step and change a parameter or a particular data transform to see the effect on later performance.

In order to do so, it is important to factor in the cyclic nature of this process in your code. Write code with well-designed abstractions with each component being independently reusable.

Let's expand a little bit into each of these sections.