In the previous chapter, you learned how Amazon SageMaker Autopilot makes it easy to build, train, and optimize models automatically, without writing a line of machine learning code.
For problem types that are not supported by SageMaker Autopilot, the next best option is to use one of the algorithms already implemented in SageMaker, and to train it on your dataset. These algorithms are referred to as built-in algorithms, and they cover many typical machine learning problems, from classification to time series to anomaly detection.
In this chapter, you will learn about built-in algorithms for supervised and unsupervised learning, what type of problems you can solve with them, and how to use them with the SageMaker SDK:
- Discovering the built-in algorithms in Amazon SageMaker
- Training and deploying models with built-in algorithms
- Using the SageMaker SDK with built-in algorithms
- Working with more built-in algorithms ...