Summary

In this chapter, we discussed how a data science environment can provide a scalable infrastructure for experimentation, model training, and model deployment for testing purposes. You learned about the core architecture components for building a fully managed data science environment using AWS services such as Amazon SageMaker, Amazon ECR, AWS CodeCommit, and Amazon S3. You also practiced setting up a data science environment and trained and deployed an NLP model using both SageMaker Studio Notebook and SageMaker Training Service. At this point, you should be able to talk about the key components of a data science environment, as well as how to build one using AWS services and use it for model building, training, and deployment. In the next chapter, we will talk about how to build an enterprise ML platform for scale through automation.