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

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.

在 AWS 上無縫地使用生產就緒的機器學習系統和流程，解決機器學習生命週期中遇到的關鍵問題。

主要特點：

- 通過使用 Amazon SageMaker、Amazon EKS 等 AWS 服務，獲得在 AWS 上管理機器學習工作負載的實用知識。
- 使用容器和無伺服器服務解決各種機器學習工程需求。
- 在 AWS 上設計、構建和保護自動化的 MLOps 流程和工作流。

書籍描述：

隨著對在雲端自動化複雜的 MLOps 流程以及具有機器學習工程需求的專業人員的需求不斷增加，對於具有在機器學習工程需求上工作經驗以及了解如何在雲端自動化複雜的 MLOps 流程的知識的專業人員的需求也越來越大。本書探討了多種 AWS 服務，例如 Amazon Elastic Kubernetes Service、AWS Glue、AWS Lambda、Amazon Redshift 和 AWS Lake Formation，這些服務可以讓機器學習從業人員在生產環境中滿足各種數據工程和機器學習工程需求。

這本機器學習書籍涵蓋了基本概念以及逐步指導，旨在幫助您對如何在雲端管理和保護機器學習工作負載有扎實的理解。隨著您逐步深入章節，您將發現如何在 AWS 上訓練和部署 TensorFlow 和 PyTorch 深度學習模型時使用多個容器和無伺服器解決方案。同時，您還將深入探討成本優化技術、數據隱私和模型隱私保護策略，並探索在使用每個 AWS 服務時的最佳實踐。

通過閱讀本書，您將能夠構建、擴展和保護自己的機器學習系統和流程，這將為您提供在機器學習工程需求中使用各種 AWS 服務架構自定義解決方案所需的經驗和信心。

您將學到什麼：

- 了解如何在 AWS 上訓練和部署 TensorFlow 和 PyTorch 模型。
- 使用容器和無伺服器服務滿足機器學習工程需求。
- 發現如何在 AWS 上建立無伺服器數據倉庫和數據湖。
- 使用多種服務構建自動化的端到端 MLOps 流程。
- 使用 AWS Glue DataBrew 和 SageMaker Data Wrangler 進行數據工程。
- 探索在 AWS 上部署深度學習模型的不同解決方案。
- 在機器學習環境和系統中應用成本優化技術。
- 使用多種技術保護數據隱私和模型隱私。

本書適合對機器學習工程、數據科學和 AWS 雲端工程感興趣的機器學習工程師、數據科學家和 AWS 雲端工程師，他們希望在生產數據工程、機器學習工程和 MLOps 需求中使用各種 AWS 服務，例如 Amazon EC2、Amazon Elastic Kubernetes Service (EKS)、Amazon SageMaker、AWS Glue、Amazon Redshift、AWS Lake Formation 和 AWS Lambda。您只需要一個 AWS 帳戶即可開始。事先了解 AWS、機器學習和 Python 編程語言的知識將有助於更有效地理解本書中涵蓋的概念。

1. Introduction to ML Engineering on AWS
2. Deep Learning AMIs
3. Deep Learning Containers
4. Serverless Data Management on AWS
5. Pragmatic Data Processing and Analysis
6. SageMaker Training and Debugging Solutions
7. SageMaker Deployment Solutions
8. Model Monitoring and Management Solutions
9. Security, Governance, and Compliance Strategies
10. Machine Learning Pipelines with Kubeflow on Amazon EKS
11. Machine Learning Pipelines with SageMaker Pipelines

• 在AWS上的機器學習工程介紹


• 深度學習AMI


• 深度學習容器


• AWS上的無伺服器資料管理


• 實用的資料處理和分析


• SageMaker培訓和除錯解決方案


• SageMaker部署解決方案


• 模型監控和管理解決方案


• 安全、治理和合規策略


• 在Amazon EKS上使用Kubeflow的機器學習流程


• 使用SageMaker Pipelines的機器學習流程