In the previous chapter, you learned how Amazon SageMaker helps you build and prepare datasets. In a typical machine learning project, the next step would be to start experimenting with algorithms in order to find an early fit and get a sense of the predictive power you could expect from the model.
Whether you work with statistical machine learning or deep learning, three options are available when it comes to selecting an algorithm:
- Write your own, or customize an existing one. This only makes sense if you have strong skills in statistics and computer science, if you're quite sure that you can do better than well-tuned, off-the-shelf algorithms, and if you're given enough time to work on the project. Let's face it, these conditions are rarely met.
- Use a built-in algorithm implemented in one of your favorite libraries, such as Linear Regression or XGBoost. For deep learning problems, this includes pretrained...