Summary
In this chapter, we discussed all aspects of ML within AWS Glue. We talked about Glue ML transforms, what they are, how they are trained, and how they can be used. We also discussed AWS SageMaker and how it can integrate with Glue resources to accelerate the execution of ML code in notebooks. Finally, we analyzed reference architectures and services for ML pipelines using AWS Glue and SageMaker.
These concepts should have given you a complete overview of how Glue can be used for ML purposes, and how Glue can fit into your ML architecture in the AWS cloud. In the next chapter, we will talk about the data lake architecture and designing use cases for real-world scenarios.
