書名： Learn Amazon SageMaker
作者名： Julien Simon;Francesco Pochetti
本章字數(shù)： 274字
更新時間： 2021-04-09 23:11:22

Chapter 3: AutoML with Amazon SageMaker Autopilot

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 models available in TensorFlow, PyTorch, and so on. This option saves you the trouble of writing machine learning code. Instead, it lets you focus on feature engineering and model optimization.

Use AutoML, a rising technique that lets you automatically build, train, and optimize machine learning models.

In this chapter, you will learn about Amazon SageMaker Autopilot, an AutoML capability part of Amazon SageMaker. We'll see how to use it in Amazon SageMaker Studio, without writing a single line of code, and also how to use it with the Amazon SageMaker SDK:

Discovering Amazon SageMaker Autopilot

Using Amazon SageMaker Autopilot in SageMaker Studio

Using Amazon SageMaker Autopilot with the SageMaker SDK

Diving deep on Amazon SageMaker Autopilot

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Learn Amazon SageMaker

Chapter 3: AutoML with Amazon SageMaker Autopilot