Summary
In this chapter, we covered topics that went beyond compute, storage, and networking. We saw how to apply cost-optimization methods for more advanced cloud-native environments including analytics and ML.
We unpacked AWS elasticity and what that means for architecting our workload. Take advantage of auto-scaling tools on AWS. These tools themselves are free. You only pay for the resources provisioned by scale-out activities and benefit by not paying for terminated resources from scale-in events. You learned about the various scaling policies and the difference between AWS Auto Scaling and Amazon EC2 Auto Scaling.
We then explored the realm of analytics. We found ways to optimize costs using compression, setting up the right data structure, and Redshift concurrency-scaling and workload management features.
Lastly, we learned about the various steps in a typical ML workload. We looked at ways to optimize data processing jobs using a managed service such as Amazon SageMaker...