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

Automated machine learning on AWS fast-track the development of your production-ready machine learning applications the AWS way

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 mac...

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Detalles Bibliográficos
Otros Autores: Potgieter, Trenton, author (author), Dahlberg, Jonathan, author
Formato: Libro electrónico
Idioma:Inglés
Publicado: Birmingham : Packt Publishing, Limited [2022]
Materias:
Amazon Web Services (Firm)
Machine learning.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009660213506719
Tabla de Contenidos:
  • 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
  • Other Books You May Enjoy.

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