Summary
In this chapter, you dived into deploying ML models with Amazon SageMaker, exploring factors influencing deployment options. You looked at real-world scenarios and dissected them to try out hands-on solutions and code snippets for diverse use cases. You emphasized the crucial integration of SageMaker deployment with AWS Auto Scaling, dynamically adjusting resources based on workload variations. You focused on securing SageMaker applications, presenting practical strategies such as VPC endpoints, IAM roles, and encryption practices. Referring to the AWS documentation for clarifying any doubts is also the best option. It is always important to design your solutions in a cost-effective way, so exploring the cost-effective way to use these services is equally important as building the solution.
