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Automated Machine Learning on AWS

Automated Machine Learning on AWS

Trenton Potgieter

Kup pozycję na ebookpoint.pl

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.

  • Automated Machine Learning on AWS
  • Foreword
  • Contributors
  • About the author
  • About the reviewer
  • Preface
    • Who this book is for
    • What this book covers
    • To get the most out of this book
    • Download the example code files
    • Download the color images
    • Conventions used
    • Get in touch
    • Share Your Thoughts
  • 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 pipelines 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
    • Why subscribe?
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