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

Book Description

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 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 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.

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 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.

中文：

书名：AWS上的机器学习工程: 在生产中构建、扩展和保护机器学习系统和MLOps管道

通过解决ML生命周期中遇到的关键痛点，与AWS上的生产就绪机器学习系统和管道无缝合作

主要功能

• 获得实际知识管理ML工作负载在AWS使用Amazon SageMaker，Amazon EKS，和更多
• 使用容器和serverless服务解决各种ML工程需求
• 在AWS上设计、构建和保护自动化MLOps管道和工作流

图书描述

对具有机器学习 (ML) 工程要求经验的专业人员以及具有自动化云中复杂MLOps管道知识的专业人员的需求日益增长。本书探讨了各种AWS服务，如Amazon Elastic Kubernetes服务、AWS Glue、AWS Lambda、Amazon Redshift和AWS Lake Formation，ML从业者可以利用这些服务来满足生产中的各种数据工程和ML工程要求。

这本机器学习书涵盖了基本概念以及分步说明，旨在帮助您深入了解如何管理和保护云中的ML工作负载。随着章节的进展，您将发现在AWS上培训和部署TensorFlow和PyTorch深度学习模型时如何使用几种容器和无服务器解决方案。当您探索使用每个AWS时的最佳实践时，您还将详细研究经过验证的成本优化技术以及数据隐私和模型隐私保护策略。

到本AWS书结束时，您将能够构建、扩展和保护自己的ML系统和管道，这将为您提供使用各种AWS服务来满足ML工程要求的定制解决方案设计所需的经验和信心。

什么你会学到

• 了解如何在AWS上训练和部署TensorFlow和PyTorch模型
• 使用容器和serverless服务满足ML工程要求
• 了解如何在AWS上设置无服务器数据仓库和数据湖
• 使用多种服务构建自动化的端到端MLOps管道
• 使用AWS Glue DataBrew和SageMaker数据牧马人进行数据工程
• 探索在AWS上部署深度学习模型的不同解决方案
• 将成本优化技术应用于ML环境和系统
• 使用多种技术保护数据隐私和模型隐私

这本书是为谁准备的

本书面向机器学习工程师，数据科学家和AWS云工程师，他们对使用各种AWS服务 (例如Amazon EC2，Amazon Elastic Kubernetes Service (EKS)，Amazon SageMaker，AWS Glue，Amazon Redshift，AWS Lake Formation和AWS Lambda-您只需要一个AWS帐户即可开始使用。对AWS，机器学习和Python编程语言的先验知识将帮助您更有效地掌握本书中涉及的概念。