Work seamlessly with production-ready machine learning systems and pipelines on AWS by addressing key pain points encountered in the ML life cycle Key Features Gain practical knowledge of managing ML workloads on AWS using Amazon SageMaker, Amazon EKS, and more Use container and serverless services to solve a variety of ML engineering requirements Design, build, and secure automated MLOps pipelines and workflows on AWS Book DescriptionThere is a growing need for professionals with experience in working on machine learning (ML) engineering requirements as well as those with knowledge of...