Build, scale, and secure production-ready machine learning systems and pipelines on AWS addressing the key pain points encountered in the ML life cycle Key Features * Gain practical knowledge managing ML workloads on AWS using Amazon SageMaker, Amazon EKS, and more * Learn how to 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 Description There is a growing need for professionals who have experience working on machine learning engineering requirements as well as knowledge of automating complex MLOps pipelines in the cloud. In this book, we will explore a variety of AWS services, such as Amazon SageMaker, Amazon Elastic Kubernetes Service, AWS Glue, AWS Lambda, Amazon Redshift, and AWS LakeFormation, which ML practitioners can use to solve various data engineering and ML engineering requirements in production. This book features the essential concepts as well as the step-by-step instructions designed to give you a solid understanding of how to manage and secure ML workloads in the cloud. You will also learn how to use several container and serverless solutions when training and deploying TensorFlow and PyTorch deep learning models on AWS. Proven cost optimization techniques as well as data privacy and model privacy preservation strategies will be discussed in detail as we dive deeper into the best practices when using each AWS service. By the end of this book, you will be able to build, scale, and secure your own ML systems and pipelines addressing the key pain points encountered in the ML life cycle. This should give you the experience and confidence needed to architect custom solutions using a variety of AWS services for ML engineering requirements. What you will learn * Learn how to train and deploy TensorFlow and PyTorch models on AWS * Use containers and serverless services for ML engineering requirements * Learn how to set up a serverless data warehouse and data lake on AWS * Build automated end-to-end MLOps pipelines using a variety of services * Use AWS Glue DataBrew and SageMaker Data Wrangler for data engineering * Explore different solutions for deploying deep learning models on AWS * Apply cost optimization techniques to ML environments and systems * Preserve data privacy and model privacy using a variety of techniques Who This Book Is For This book is for machine learning engineers, data scientists, and AWS cloud engineers interested in working on production data engineering, machine learning engineering, and MLOps requirements using a variety of AWS Services such as Amazon EC2, Amazon Elastic Kubernetes Service (EKS), Amazon SageMaker, AWS Glue, Amazon Redshift, AWS LakeFormation, and AWS Lambda. All you need is an AWS account to get things running. Prior knowledge of AWS, machine learning, and Python programming language will help you to grasp the concepts covered in this book more effectively.
Book InformationISBN 9781803247595
Author Joshua Arvin LatFormat Paperback
Page Count 570
Imprint Packt Publishing LimitedPublisher Packt Publishing Limited
Weight(grams) 75g