Why does the the problem need to be solved in ML?

Data Science, AI and ML
#1

Motivation

Consider your motivation for solving the problem. What need will be fulfilled when the problem is solved?

For example, you may be solving the problem as a learning exercise. This is useful to clarify as you can decide that you don’t want to use the most suitable method to solve the problem, but instead you want to explore methods that you are not familiar with in order to learn new skills.

Alternatively, you may need to solve the problem as part of a duty at work, ultimately to keep your job.

Solution Benefits

Consider the benefits of having the problem solved. What capabilities does it enable?

It is important to be clear on the benefits of the problem being solved to ensure that you capitalize on them. These benefits can be used to sell the project to colleagues and management to get buy in and additional time or budget resources.

If it benefits you personally, then be clear on what those benefits are and how you will know when you have got them. For example, if it’s a tool or utility, then what will you be able to do with that utility that you can’t do now and why is that meaningful to you?

Solution Use

Consider how the solution to the problem will be used and what type of lifetime you expect the solution to have. As programmers we often think the work is done as soon as the program is written, but really the project is just beginning it’s maintenance lifetime.

The way the solution will be used will influence the nature and requirements of the solution you adopt.

Consider whether you are looking to write a report to present results or you want to operationalize the solution. If you want to operationalize the solution, consider the functional and nonfunctional requirements you have for a solution, just like a software project.