pstrongWork seamlessly with production-ready machine learning systems and pipelines on AWS by addressing key pain points encountered in the ML life cyclestrongppbrppstrongKey Features strongpulliGain practical knowledge of managing ML workloads on AWS using Amazon SageMaker, Amazon EKS, and moreliliUse container and serverless services to solve a variety of ML engineering requirementsliliDesign, build, and secure automated MLOps pipelines and workflows on AWSliulpbrppstrongBook Description strongppThere is a growing need for professionals with experience in working on machine learning ML engineering requirements as well as those with knowledge of automating complex MLOps pipelines in the cloud. This book explores a variety of AWS services, such as Amazon Elastic Kubernetes Service, AWS Glue, AWS Lambda, Amazon Redshift, and AWS Lake Formation, which ML practitioners can leverage to meet various data engineering and ML engineering requirements in production.ppThis machine learning book