Preface

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 this book is for

This book is for ML 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 this book covers

Chapter 1, Introduction to ML Engineering on AWS, focuses on helping you get set up, understand the key concepts, and get your feet wet quickly with several simplified AutoML examples.

Chapter 2, Deep Learning AMIs, introduces AWS Deep Learning AMIs and how they are used to help ML practitioners perform ML experiments faster inside EC2 instances. Here, we will also dive a bit deeper into how AWS pricing works for EC2 instances so that you will have a better idea of how to optimize and reduce the overall costs of running ML workloads in the cloud.

Chapter 3, Deep Learning Containers, introduces AWS Deep Learning Containers and how they are used to help ML practitioners perform ML experiments faster using containers. Here, we will also deploy a trained deep learning model inside an AWS Lambda function using Lambda’s container image support.

Chapter 4, Serverless Data Management on AWS, presents several serverless solutions, such as Amazon Redshift Serverless and AWS Lake Formation, for managing and querying data on AWS.

Chapter 5, Pragmatic Data Processing and Analysis, focuses on the different services available when working on data processing and analysis requirements, such as AWS Glue DataBrew and Amazon SageMaker Data Wrangler.

Chapter 6, SageMaker Training and Debugging Solutions, presents the different solutions and capabilities available when training an ML model using Amazon SageMaker. Here, we dive a bit deeper into the different options and strategies when training and tuning ML models in SageMaker.

Chapter 7, SageMaker Deployment Solutions, focuses on the relevant deployment solutions and strategies when performing ML inference on the AWS platform.

Chapter 8, Model Monitoring and Management Solutions, presents the different monitoring and management solutions available on AWS.

Chapter 9, Security, Governance, and Compliance Strategies, focuses on the relevant security, governance, and compliance strategies needed to secure production environments. Here, we will also dive a bit deeper into the different techniques to ensure data privacy and model privacy.

Chapter 10, Machine Learning Pipelines with Kubeflow on Amazon EKS, focuses on using Kubeflow Pipelines, Kubernetes, and Amazon EKS to deploy an automated end-to-end MLOps pipeline on AWS.

Chapter 11, Machine Learning Pipelines with SageMaker Pipelines, focuses on using SageMaker Pipelines to design and build automated end-to-end MLOps pipelines. Here, we will apply, combine, and connect the different strategies and techniques we learned in the previous chapters of the book.

To get the most out of this book

You will need an AWS account and a stable internet connection to complete the hands-on solutions in this book. If you still do not have an AWS account, feel free to check the AWS Free Tier page and click Create a Free Account: https://aws.amazon.com/free/.

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

Download the example code files

You can download the example code files for this book from GitHub at https://github.com/PacktPublishing/Machine-Learning-Engineering-on-AWS. If there’s an update to the code, it will be updated in the GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://packt.link/jeBII.

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “ENTRYPOINT is set to /opt/conda/bin/python -m awslambdaric. The CMD command is then set to app.handler. The ENTRYPOINT and CMD instructions define which command is executed when the container starts to run.”

A block of code is set as follows:

SELECT booking_changes, has_booking_changes, * 
FROM dev.public.bookings 
WHERE 
(booking_changes=0 AND has_booking_changes='True') 
OR 
(booking_changes>0 AND has_booking_changes='False');

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

---
apiVersion: eksctl.io/v1alpha5
kind: ClusterConfig
metadata:
  name: kubeflow-eks-000
  region: us-west-2
  version: "1.21"
availabilityZones: ["us-west-2a", "us-west-2b", "us-west-2c", "us-west-2d"]
managedNodeGroups:
- name: nodegroup
  desiredCapacity: 5
  instanceType: m5.xlarge
  ssh:
    enableSsm: true

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: “After clicking the FILTER button, a drop-down menu should appear. Locate and select Greater than or equal to from the list of options under By condition. This should update the pane on the right side of the page and show the list of configuration options for the Filter values operation.”

Tips or Important Notes

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Get in touch

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