Summary
In this chapter, we learned about the usage of SageMaker for creating notebook instances and training instances. As we went through we learned how to use SageMaker for hyperparameter tuning jobs. As the security of our assets in AWS is an essential part, we learned about the various ways to secure SageMaker instances. With hands-on practices, we created Step Functions and orchestrated our pipeline using AWS Lambda.
AWS products are evolving every day to help us solve our IT problems. It's not easy to remember all the product names. The only way to learn is through practice. When you're solving a problem or building a product, then focus on the different technological areas of your product. Those areas can be an AWS service, for example, scheduling jobs, logging, tracing, monitoring metrics, autoscaling, and more.
Compute time, storage, and networking are the baselines. It is recommended that you practice some examples for each of these services. Referring to...