Not reinventing the wheel

You will already have noticed through this chapter (or I hope you have!) that a lot of the functionality that you need for your ML and Python project has already been built. One of the most important things you can learn as an ML engineer is that you are not supposed to build everything from scratch. You can do this in a variety of ways, the most obvious of which is to use other packages in your own solution and then build functionality that enriches what is already there. As an example, you do not need to build basic regression modeling capabilities since they exist in a variety of packages, but you might have to add a new type of regressor or use some specific domain knowledge or trick you have developed. In this case, you would be justified in writing your own code on top of the existing solution. You can also use a variety of concepts from Python, such as wrapper classes or decorators, as well. The key message is that although there is a lot of work for...