Summary
In this chapter, we explored 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, and Amazon S3. You practiced setting up a data science environment and trained and deployed an NLP model using both SageMaker Studio notebooks and the SageMaker Training service. You have also developed hands-on experience with SageMaker Canvas to automate ML tasks from model building to model deployment.
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.
