Machine Learning Engineering on AWS: Build, scale, and secure machine learning systems and MLOps pipelines in production
by Joshua Arvin Lat
Publisher finelybook 出版社: Packt Publishing (October 27, 2022)
Language 语言: English
Print Length 页数: 530 pages
ISBN-10: 1803247592
ISBN-13: 9781803247595
Book Description
By finelybook
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
By finelybook
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.
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 Amazon EKS
Machine Learning Pipelines with SageMaker Pipelines
