{"product_id":"machine-learning-engineering-on-aws-build-scale-and-secure-machine-learning-systems-and-mlops-pipelines-in-production-paperback","title":"Machine Learning Engineering on AWS: Build, scale, and secure machine learning systems and MLOps pipelines in production - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eJoshua Arvin Lat\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWork seamlessly with production-ready machine learning systems and pipelines on AWS by addressing key pain points encountered in the ML life cycle\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eKey Features: \u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eGain practical knowledge of managing ML workloads on AWS using Amazon SageMaker, Amazon EKS, and more\u003c\/li\u003e\n\u003cli\u003eUse container and serverless services to solve a variety of ML engineering requirements\u003c\/li\u003e\n\u003cli\u003eDesign, build, and secure automated MLOps pipelines and workflows on AWS\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eBook Description: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eThere 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.\u003c\/p\u003e\u003cp\u003eThis machine learning book covers the essential concepts as well as step-by-step instructions that are designed to help you get a solid understanding of how to manage and secure ML workloads in the cloud. As you progress through the chapters, you'll discover how to use several container and serverless solutions when training and deploying TensorFlow and PyTorch deep learning models on AWS. You'll also delve into proven cost optimization techniques as well as data privacy and model privacy preservation strategies in detail as you explore best practices when using each AWS.\u003c\/p\u003e\u003cp\u003eBy the end of this AWS book, you'll be able to build, scale, and secure your own ML systems and pipelines, which will give you the experience and confidence needed to architect custom solutions using a variety of AWS services for ML engineering requirements.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWhat You Will Learn: \u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eFind out how to train and deploy TensorFlow and PyTorch models on AWS\u003c\/li\u003e\n\u003cli\u003eUse containers and serverless services for ML engineering requirements\u003c\/li\u003e\n\u003cli\u003eDiscover how to set up a serverless data warehouse and data lake on AWS\u003c\/li\u003e\n\u003cli\u003eBuild automated end-to-end MLOps pipelines using a variety of services\u003c\/li\u003e\n\u003cli\u003eUse AWS Glue DataBrew and SageMaker Data Wrangler for data engineering\u003c\/li\u003e\n\u003cli\u003eExplore different solutions for deploying deep learning models on AWS\u003c\/li\u003e\n\u003cli\u003eApply cost optimization techniques to ML environments and systems\u003c\/li\u003e\n\u003cli\u003ePreserve data privacy and model privacy using a variety of techniques\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWho this book is for: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eThis 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 Lake Formation, and AWS Lambda -- all you need is an AWS account to get started. Prior knowledge of AWS, machine learning, and the Python programming language will help you to grasp the concepts covered in this book more effectively.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 530\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 1.07 x 9.25 x 7.5 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e October 27, 2022\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":47212056805625,"sku":"9781803247595","price":70.54,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0789\/2782\/3097\/files\/SlpIeFk4NVZBQWx0RU1yTmlGbGlNdz09.webp?v=1768087938","url":"https:\/\/bookscloud.io\/products\/machine-learning-engineering-on-aws-build-scale-and-secure-machine-learning-systems-and-mlops-pipelines-in-production-paperback","provider":"BooksCloud Book Dropshipping","version":"1.0","type":"link"}