2 Is there a problem?
This chapter covers
- Problem space and solution space: which comes first?
- Defining a problem as the most important step
- Defining risks and limitations
- Costs of a mistake
To succeed in machine learning system design, you literally need to be an expert in multiple fields, combining the roles and expertise of a software engineer, product manager, and AI specialist. However, when stripped down to the bones, even the most complex and sophisticated solutions in ML system design will have the same framework and fundamentals as any other product.
The variety and amount of sheer knowledge gained over the past years gives you an unprecedented freedom to choose exactly the approach you want towards your ML system, but no matter how refined the instruments of your choice are, they’re no more than implementation mediums.
What are the business goals? How big is the budget? How flexible are the deadlines? Will the potential output cover and exceed overall costs? These are among the crucial questions that you need to ask yourself before scoping your ML project.