Automated Machine Learning on AWS: Fast-track the development of your production-ready machine learning applications the AWS way

✍ Scribed by Trenton Potgieter

• Publisher: Packt Publishing
• Year: 2022
• Tongue: English
• Leaves: 421
• Category: Library

✦ Synopsis

Automate the process of building, training, and deploying machine learning applications to production with AWS solutions such as SageMaker Autopilot, AutoGluon, Step Functions, Amazon Managed Workflows for Apache Airflow, and more

Key Features

• Explore the various AWS services that make automated machine learning easier
• Recognize the role of DevOps and MLOps methodologies in pipeline automation
• Get acquainted with additional AWS services such as Step Functions, MWAA, and more to overcome automation challenges

Book Description

AWS provides a wide range of solutions to help automate a machine learning workflow with just a few lines of code. With this practical book, you'll learn how to automate a machine learning pipeline using the various AWS services.

Automated Machine Learning on AWS begins with a quick overview of what the machine learning pipeline/process looks like and highlights the typical challenges that you may face when building a pipeline. Throughout the book, you'll become well versed with various AWS solutions such as Amazon SageMaker Autopilot, AutoGluon, and AWS Step Functions to automate an end-to-end ML process with the help of hands-on examples. The book will show you how to build, monitor, and execute a CI/CD pipeline for the ML process and how the various CI/CD services within AWS can be applied to a use case with the Cloud Development Kit (CDK). You'll understand what a data-centric ML process is by working with the Amazon Managed Services for Apache Airflow and then build a managed Airflow environment. You'll also cover the key success criteria for an MLSDLC implementation and the process of creating a self-mutating CI/CD pipeline using AWS CDK from the perspective of the platform engineering team.

By the end of this AWS book, you'll be able to effectively automate a complete machine learning pipeline and deploy it to production.

What you will learn

• Employ SageMaker Autopilot and Amazon SageMaker SDK to automate the machine learning process
• Understand how to use AutoGluon to automate complicated model building tasks
• Use the AWS CDK to codify the machine learning process
• Create, deploy, and rebuild a CI/CD pipeline on AWS
• Build an ML workflow using AWS Step Functions and the Data Science SDK
• Leverage the Amazon SageMaker Feature Store to automate the machine learning software development life cycle (MLSDLC)
• Discover how to use Amazon MWAA for a data-centric ML process

Who this book is for

This book is for the novice as well as experienced machine learning practitioners looking to automate the process of building, training, and deploying machine learning-based solutions into production, using both purpose-built and other AWS services. A basic understanding of the end-to-end machine learning process and concepts, Python programming, and AWS is necessary to make the most out of this book.

Table of Contents

1. Getting Started with Automated Machine Learning on AWS
2. Automating Machine Learning Model Development Using SageMaker Autopilot
3. Automating Complicated Model Development with AutoGluon
4. Continuous Integration and Continuous Delivery (CI/CD) for Machine Learning
5. Continuous Deployment of a Production ML Model
6. Automating the Machine Learning Process Using AWS Step Functions
7. Building the ML Workflow Using AWS Step Functions
8. Automating the Machine Learning Process Using Apache Airflow
9. Building the ML Workflow Using Amazon Managed Workflows for Apache Airflow
10. An Introduction to the Machine Learning Software Development Lifecycle (MLSDLC)
11. Continuous Integration, Deployment, and Training for the MLSDLC

✦ Table of Contents

Cover
Title Page
Copyright and Credits
Foreword
Contributors
Table of Contents
Preface
Section 1: Fundamentals of the Automated Machine Learning Process and AutoML on AWS
Chapter 1: Getting Started with Automated Machine Learning on AWS
Technical requirements
Overview of the ML process
Complexities in the ML process
An example of the end-to-end ML process
Introducing ACME Fishing Logistics
The case for ML
Getting insights from the data
Building the right model
Training the model
Evaluating the trained model
Exploring possible next steps
Tuning our model
Deploying the optimized model into production
Streamlining the ML process with AutoML
How AWS makes automating the ML development and deployment process easier
Summary
Chapter 2: Automating Machine Learning Model Development Using SageMaker Autopilot
Technical requirements
Introducing the AWS AI and ML landscape
Overview of SageMaker Autopilot
Overcoming automation challenges with SageMaker Autopilot
Getting started with SageMaker Studio
Preparing the experiment data
Starting the Autopilot experiment
Running the Autopilot experiment
Post-experimentation tasks
Using the SageMaker SDK to automate the ML experiment
Codifying the Autopilot experiment
Analyzing the Autopilot experiment with code
Deploying the best candidate
Cleaning up
Summary
Chapter 3: Automating Complicated Model Development with AutoGluon
Technical requirements
Introducing the AutoGluon library
Using AutoGluon for tabular data
Prerequisites
Creating the AutoML experiment with AutoGluon
Evaluating the experiment results
Using AutoGluon for image data
Prerequisites
Creating an image prediction experiment
Evaluating the experiment results
Summary
Section 2: Automating the Machine Learning Process with Continuous Integration and Continuous Delivery (CI/CD)
Chapter 4: Continuous Integration and Continuous Delivery (CI/CD) for Machine Learning
Technical requirements
Introducing the CI/CD methodology
Introducing the CI part of CI/CD
Introducing the CD part of CI/CD
Closing the loop
Automating ML with CI/CD
Taking a deployment-centric approach
Creating an MLOps methodology
Creating a CI/CD pipeline on AWS
Using the AWS CI/CD toolchain
Working with additional AWS developer tools
Creating a cloud-native CI/CD pipeline for a production ML model
Preparing the development environment
Creating the pipeline artifact repository
Developing the application artifacts
Summary
Chapter 5: Continuous Deployment of a Production ML Model
Technical requirements
Deploying the CI/CD pipeline
Codifying the pipeline construct
Creating the CDK application
Deploying the pipeline application
Building the ML model artifacts
Reviewing the modeling file
Reviewing the application file
Reviewing the model serving files
Reviewing the container build file
Committing the ML artifacts
Executing the automated ML model deployment
Cleanup
Summary
Section 3: Optimizing a Source Code-Centric Approach to Automated Machine Learning
Chapter 6: Automating the Machine Learning Process Using AWS Step Functions
Technical requirements
Introducing AWS Step Functions
Creating a state machine
Addressing state machine complexity
Using the Step Functions Data Science SDK for CI/CD
Building the CI/CD pipeline resources
Updating the development environment
Creating the pipeline artifact repository
Building the pipeline application artifacts
Deploying the CI/CD pipeline
Summary
Chapter 7: Building the ML Workflow Using AWS Step Functions
Technical requirements
Building the state machine workflow
Setting up the service permissions
Creating an ML workflow
Performing the integration test
Monitoring the pipeline's progress
Summary
Section 4: Optimizing a Data-Centric Approach to Automated Machine Learning
Chapter 8: Automating the Machine Learning Process Using Apache Airflow
Technical requirements
Introducing Apache Airflow
Introducing Amazon MWAA
Using Airflow to process the abalone dataset
Configuring the MWAA prerequisites
Configuring the MWAA environment
Summary
Chapter 9: Building the ML Workflow Using Amazon Managed Workflows for Apache Airflow
Technical requirements
Developing the data-centric workflow
Building and unit testing the data ETL artifacts
Building the Airflow DAG
Creating synthetic Abalone survey data
Executing the data-centric workflow
Cleanup
Summary
Section 5: Automating the End-to-End Production Application on AWS
Chapter 10: An Introduction to the Machine Learning Software Development Life Cycle (MLSDLC)
Technical requirements
Introducing the MLSDLC
Building the application platform
Examining the role of the application owner
Examining the role of the platform engineers
Examining the role of the frontend developers
Examining ML and data engineering roles
Creating a SageMaker Feature Store
Creating ML artifacts
Creating continuous training artifacts
Understanding the security lens
Securing the data
Securing the code
Securing the website
Summary
Chapter 11: Continuous Integration, Deployment, and Training for the MLSDLC
Technical requirements
Codifying the continuous integration stage
Building the integration artifacts
Building the test artifacts
Building the production artifacts
Automating the continuous integration process
Managing the continuous deployment stage
Reviewing the build phase
Reviewing the test phase
Reviewing the deploy and maintain phases
Reviewing the application user experience
Managing continuous training
Creating new Abalone survey data
Reviewing the continuous training process
Cleanup
Summary
Further reading
Index
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