Summary

As you can see, built-in algorithms are a great way to quickly train and deploy models without having to write any machine learning code.

In this chapter, you learned about the SageMaker workflow, and how to implement it with a handful of APIs from the SageMaker SDK, without ever worrying about infrastructure.

You learned how to work with data in CSV and RecordIO-wrapped protobuf format, the latter being the preferred format for large-scale training on bulky datasets.You also learned how to build models with important algorithms for supervised and unsupervised learning: Linear Learner, XGBoost, Factorization Machines, PCA, and Random Cut Forest.

In the next chapter, you will learn how to use additional built-in algorithms to build computer vision models.