Machine Learning Engineering on AWS

[LeeAndro]

English | 2022 | ISBN: 1803247592 | 1220 pages | True EPUB, MOBI | 48.53 MB

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 eeering 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) eeering 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 eeering and ML eeering 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 strats 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 eeering 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 eeering 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 eeering
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 eeers, data scientists, and AWS cloud eeers interested in working on production data eeering, machine learning eeering, 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.



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