How to Structure and Manage Natural Language Processing (NLP) Projects

How to Structure and Manage Natural Language Processing (NLP) Projects

In this post I will share key pointers, guidelines, tips and tricks that I learned while working on various data science projects. Many things can be valuable in any ML project but some are specific to NLP.

If there is one thing I learned working in the ML industry is this: machine learning projects are messy.

It is not that people don’t want to have things organized it is just there are many things that are hard to structure and manage over the course of the project. 

You may start clean but things come in the way. 

Some typical reasons are:

  • quick data explorations in Notebooks, 
  • model code taken from the research repo on github, 
  • new datasets added when everything was already set,
  • data quality issues are discovered and re-labeling of the data is needed,
  • someone on the team “just tried something quickly” and changed training parameters (passed via argparse) without telling anyone about it,
  • push to turn prototypes into production “just this once” coming from the top.

Over the years working as a machine learning engineer I’ve learned a bunch of things that can help you stay on top of things and keep your NLP projects in check (as much as you can really have ML projects in check:)). 

In this post I will share key pointers, guidelines, tips and tricks that I learned while working on various data science projects. Many things can be valuable in any ML project but some are specific to NLP. 

Key points covered: 

  • Creating a good project directory structure
  • Dealing with changing data: Data Versioning
  • Keeping track of ML experiments
  • Proper evaluation and managing metrics and KPIs
  • Model Deployment: how to get it right

Let’s jump in.

Directory structure

Data Science workflow consists of multiple elements:

  • Data, 
  • Models, 
  • Report, 
  • Training scripts, 
  • hyperparameters, 
  • and so on. 

It is often beneficial to have a common framework consistent across teams. Most likely you’d have multiple team members to work on the same project. 

There are many ways to get started with structuring your Data Science project. You can even create a custom template with some specific requirements of your team. 

However, one of the easiest and quickest ways is to use cookie-cutter template. It automatically generates a comprehensive project directory for you.

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