Best practices
The first part of this book contains significantly more best practices, which is because these best practices relate to engineering software, designing it, and making crucial decisions about machine learning – for example, the first best practice tells us when to use (and not to use) machine learning.
As this part of this book was about the machine learning landscape in software engineering, we’ll discuss different types of models and data and show how they come together.
The list of best practices from the first part of this book is presented in Table 17.1:
|
ID |
Best Practice |
|
1 |
Use machine learning algorithms when your problem is focused on data, not on the algorithm. |
|
2 |
Before you start developing a machine learning system, do due diligence and identify the right... |
