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 ...
