Natural Language Processing Project Planner

Planning data science projects is tricky, and NLP projects can be particularly problematic. Based on our past experience, we have shared an interactive tool which you can use for estimating task durations and dependencies for an NLP project.

It generates a graphical Gantt chart for your project, based on the inputs you give it.

Example Gantt chart for an NLP project

Input the parameters of your Natural Language Processing project

Project and organisation level

What is the goal of the project?
Is the client a large organisation with a complex process of procurements, purchase orders, approvals, etc?
Does the project need to be signed off by a separate executive level in the organisation, or in another organisation?
Who will use the model?


Is the text data multilingual?
Does the text data need to be extracted from PDFs or similar?
Do we need to manually annotate data?
Is the text data sensitive?
Must the data remain on the client's servers?
Is there a risk of AI bias, or is AI bias an issue?


Do we need to classify data into more than 10 classes?
Do we need to extract multiple values from text, such as finding percentages, dosages, addresses, names?
Does a gold standard of model performance exist? For example, do human annotators achieve 85% accuracy?


Must a front end program be developed?
Must the model be deployed and integrated into the existing technology stack?
Does the model need to be retrained regularly?
Do we need to make an explainable AI model?

View your NLP project’s Gantt chart

  • month 1
  • month 2
  • month 3
  • month 4
  • month 5
  • month 6
  • month 7
  • month 8
  • month 9
  • month 10
  • month 11
  • month 12
  • month 13
  • month 14
  • month 15
  • month 16
  • month 17
  • month 18
  • month 19
  • month 20
  •  NDAs
  •  ethics and privacy management
  •  request access to data and systems
  •  kick off meeting
  •  explore data
  •  label data
  •  define metrics for success
  •  develop baseline model
  •  develop a series of models in a leaderboard
  •  select best model
  •  develop front end
  •  deploy model
  •  QA
  •  user testing
  •  handover

Getting your Natural Language Processing project off the ground

Now you’ve made a draft Gantt chart for your NLP project, you can start getting everything together to launch the project. Have a look at this list of things you need to consider when starting a data science project. We also have an overview of the key stages of a data science project.

Finally, don’t forget that data science and NLP involve many unknowns. In fact, only 15-20% of projects ever complete. Please take a look at some of the resources on our website to reduce the risk, and don’t hesitate to contact Fast Data Science if you need a delivery partner for your NLP or data science project.

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