How can you maintain AI project documentation in an agile way?

Maintaining AI project documentation agilely is crucial for effective collaboration and knowledge sharing. Documenting the project vision, scope, features, data, model, and deployment details helps communicate with stakeholders. It also aids in coordinating tasks, providing feedback, and learning from the project. Helping clients understand the value of your AI system involves communicating how it addresses their needs and provides tangible benefits. Demonstrating the results and impact of your AI system through metrics, case studies, or testimonials can further enhance their understanding. By following these practices, you can maintain effective documentation and value of your AI projects. ??????

AI project documentation in an agile way should focus on the value, goals, and outcomes of each iteration, as well as the key decisions, assumptions, risks, and dependencies that affect the AI solution. Documentation should be concise, collaborative, and updated frequently to reflect the changing needs and feedback of the stakeholders and users. Documentation should also include the data sources, models, training metrics, evaluation metrics, risks and ethical considerations of the AI project.

-Incorporate visual aids like diagrams to depict ML workflows; they can often convey complex logic more quickly and clearly than text alone. -Keep documentation straightforward: A well-organized Notion page with a comprehensive README can often suffice for engineers to grasp the project's essentials.

In essence, project documentation is an ongoing real-time process. It can best be accomplished via a digital dashboard type of process which everyone can follow to keep up to date.

A further step to take so that documentation remains relevant is to provide clarity on when each piece of documentation was last updated. An entire document may be recent, however elements of it may be months or years old. Sectional versioning and time stamping can help with this process, along with reminders to update individual sections if they are moving targets.

Aside from all the logical reasons why AI projects should be documented. A very humane response to why we should document an AI project is to make life easier for new teammates who are getting onboarded. Coding requires skills, but understanding undocumented pieces of AI code requires hours of patience and willpower. To work efficiently and productively, documentation is a basic need. The time spent documenting is an investment towards the health of your AI project. I feel very passionately about this because I've worked on AI projects without documentation. When given excellent documentation, it helps your team work together seamlessly, and when your team is working together in harmony - it inadvertently leads to innovation.

Another aspect of good documentation of code/AI related stuff is able to visualise the complete workflow/process so that fine tuning ideas come naturally in mind. If somebody go through his/her own code later, can feel how it can be improved, whether any redundancy is present or not. Also it makes reviewers life easier and can contribute effectively.

AI project documentation is essential for ensuring the quality, reliability, and explainability of the AI system, as well as facilitating collaboration, communication, and learning among the stakeholders. Traditional documentation methods may not be compatible with the agile principles of delivering value, responding to change, and embracing feedback. Key things to be documented include : purpose, scope, and goals of the AI project, the data sources, quality, and processing methods, the user needs, expectations, and feedback.

Should this not be 'how do I maintain Agile project documentation in an AI way'? let our Ai friend maintain the documentation.

Documentation created for one set of people, like users or developers, can surprisingly be useful for other situations as well. User documentation I've created has been repurposed for sales pitches, demos, and proposals. This helps the multiple individuals involved to save significant time and energy.

Reviewing and updating documents is key! Without it the content of the documentation will become outdated by time. But you need a set of protocols that govern how to review & update, who should review, when to review and who is going to sign off the update. Without the protocols, you risk running into several issues such as: duplicate documents, wrong content & errors and uncomplete documentation.

Agile isn't the answer to everything and you should think carefully if your organization or team will actually benefit by adopting agile methodology. There are both countless success and horror stories - as a manager, you should know your team best: never follow a path blindly.