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

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

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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, youll discover how to use several container and serverless solutions when training and deploying TensorFlow and PyTorch deep learning models on AWS. Youll 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, youll 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
  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

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Machine Learning Engineering on AWS Build scale and secure machine learning - photo 1
Machine Learning Engineering on AWS

Build, scale, and secure machine learning systems and MLOps pipelines in production

Joshua Arvin Lat

BIRMINGHAMMUMBAI Machine Learning Engineering on AWS Copyright 2022 Packt - photo 2

BIRMINGHAMMUMBAI

Machine Learning Engineering on AWS
Copyright 2022 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

Publishing Product Manager: Ali Abidi

Content Development Editor: Priyanka Soam

Technical Editor: Devanshi Ayare

Copy Editor: Safis Editing

Project Coordinator: Farheen Fathima

Proofreader: Safis Editing

Indexer: Sejal Dsilva

Production Designer: Ponraj Dhandapani

Marketing Coordinators: Shifa Ansari

First published: October 2022

Production reference: 1290922

Published by Packt Publishing Ltd.

Livery Place

35 Livery Street

Birmingham

B3 2PB, UK.

ISBN 978-1-80324-759-5

Contributors
About the author

Joshua Arvin Lat is the Chief Technology Officer (CTO) of NuWorks Interactive Labs, Inc. He previously served as the CTO of three Australian-owned companies and also served as Director of Software Development and Engineering for multiple e-commerce start-ups in the past, which allowed him to be more effective as a leader. Years ago, he and his team won first place in a global cybersecurity competition with their published research paper. He is also an AWS Machine Learning Hero and has shared his knowledge at several international conferences, discussing practical strategies on machine learning, engineering, security, and management.

About the reviewers

Raphael Jambalos manages the Cloud-Native Development Team at eCloudValley, Philippines. His team architects and implements solutions that leverage AWS services to deliver reliable applications. He is also a community leader for the AWS user group MegaManila, organizing monthly meetups and growing the community. In his free time, he loves to read books and write about tech on his blog ( ). He holds five AWS certifications and is an AWS APN Ambassador for the Philippines. He was also a technical reviewer for the Packt book Machine Learning with Amazon SageMaker Cookbook .

Sophie Soliven is the General Manager of E-commerce Services and Dropship for BeautyMnl. As one of the pioneers and leaders of the company, she contributed to its growth from its humble beginnings to what it is today the biggest homegrown e-commerce platform in the Philippines by using a data-driven approach to scale its operations. She has obtained a number of certifications on data analytics and cloud computing, including Microsoft Power BI Data Analyst Associate, Tableau Desktop Specialist, and AWS Certified Cloud Practitioner. For the last couple of years, she has been sharing her knowledge and experience in data-driven operations at local and international conferences and events.

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