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Key benefits

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

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.

Who is this book 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.

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

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

Machine Learning with PyTorch and Scikit-Learn

£41.99

£35.99

£35.99

Total £ 113.97

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Amazon verified reviews

Adelen Dec 19, 2022

I bought this book after seeing the official AWS Machine Learning social media page share a post about it...Loved the way how the book is structured and written. Chapters generally start with a few pages of relevant concepts and then the rest of the chapter (probably around 60-70%) would focus on having the reader work on step-by-step hands-on tutorials. A must-read book if you are a data scientist or machine learning engineer.

Amazon Verified review

Dror Jan 13, 2023

This is a must-read book if you're working on or interested in machine learning (ML) engineering on AWS. At more than 500 pages, it's a comprehensive guide covering the different facets of ML engineering, with a focus on AWS and its ML-related services. The coverage is both broad and deep, starting from basic concepts such as deep learning (DL) AMIs and containers, and progressing to more advanced aspects of ML engineering for both training and deployment, including serverless solutions and helpful cost optimization techniques.I'd recommend this practical guide to every AI/ML engineer, data scientist and cloud engineer working on ML applications on AWS. A basic knowledge of AWS, ML/DL and the Python programming language will be helpful to fully grasp the more advanced topics, but are not strictly required IMO. The accompanying GitHub repo is also very useful in demonstrating the different concepts covered in the book.In summary, if you're interested in learning ML engineering on AWS, there's probably no better resource to start from. Highly recommended!

C. C Chin Jul 09, 2024

ML newbie

Amazon Customer Dec 24, 2022

Kindle version looks great in my iPad and phone. Happy to see code snippets are working as well

Nikhil Jun 09, 2023

The book is an important resource for professionals looking to build, scale, and secure ML systems and pipelines. Its practical approach, in-depth explanations, and emphasis on AWS services make it a valuable asset for anyone aiming to excel in the field of ML engineering. This book takes readers on a journey through various services, including Elastic Kubernetes Service, Glue, Lambda, Redshift, and Lake Formation. The step-by-step instructions and comprehensive coverage of essential concepts enable readers to develop a strong understanding of managing and securing ML workloads in the cloud.

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

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