Lat, Joshua Arvin Machine Learning Engineering on AWS: Build, scale, and se

Machine Learning Engineering on AWS: Build, scale, and secure machine learning systems and MLOps pipelines in production

Opis:

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 SageMaker, 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 Description There 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 Elastic Kubernetes Service, AWS Glue, AWS Lambda, Redshift, and AWS Lake Formation, which ML practitioners can leverage to meet various data engineering and ML engineering requirements in production. This 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. By 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. What you will learn Find out how to train and deploy TensorFlow and PyTorch models on AWS Use containers and serverless services for ML engineering requirements Discover 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 EC2, Elastic Kubernetes Service (EKS), SageMaker, AWS Glue, 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. Table of Contents Introduction to ML Engineering on AWS Deep Learning AMIs Deep Learning Containers Serverless Data Management on AWS Pragmatic Data Processing and Analysis SageMaker Training and Debugging Solutions SageMaker Deployment Solutions Model Monitoring and Management Solutions Security, Governance, and Compliance Strategies Machine Learning Pipelines with Kubeflow on EKS Machine Learning Pipelines with SageMaker Pipelines

Okładka: Paperback

Liczba stron:530

Autor:Lat, Joshua Arvin

Język: English: Published; English: Original Language; English

Data wydania: 2022-10-27

Waga: 2.006 gram

Wysokość: 1.1 cm

Szerokość: 7.4 cm

Długość: 9.3 cm

UWAGA: Kupując produkt na tej aukcji zgadzasz się na wydłużony termin realizacji wysyłki (10-14 dni roboczych). Podany towar pochodzi z zagranicznego magazynu, stąd wydłużony czas realizacji który podany jest obok.