How to build a roadmap in Machine Learning projects using Agile methodology in 5 simple steps?

I was recently agile coaching a team who’s aiming to improve our lives with Machine Learning solutions.

Here are the steps for a roadmap:

  1. Define your features.

When defining the features of machine learning projects, you need to keep it focused on the business benefits. The product exists to bring business value. These are the information you must provide :

  • A comprehensive title that has a meaning from the end-user perspective.

2. Measure the work. Uncertainty and poor predictability is a reality for machine learning projects. But how do we measure work in Artificial Intelligence projects?

  • Define the end to end workflow — from the requirement to make it available for the user. Here is an article I wrote on this subject.

3. Add the business value. Every project I run and every team I agile coach, I see this step done differently. It is not important how you do it, as long as it makes sense for your business, and is in a numeric format. That’s all we need for the road mapping exercise. For a real result of the portfolio roadmap, the business value independent of point 2 — the measurement of work. We are interested in knowing how important it is for your clients and your business this particular feature.

4. Rank the features. It is my favorite step in doing product road mapping. It’s the moment to bust all the myths. There is not a person who’s right or wrong, but we all find out where we should focus our energy first. HOW? Divide business value my work and then order descending. The higher number of this equation is what the team must focus their energy on. These are the features that will provide to the machine learning project stakeholders the maximum benefits with a minimum of effort.

5. Assign Stories to Sprints based on the feature ranking. Start to split in sprints from the stories belonging to the feature, which ranked the highest and keep on going until you loaded all the estimated stories. While doing this, exercise takes into consideration the scrum team capacity.

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