Automated Machine Learning on AWS: Fast-track the development of your production-ready machine learning applications the AWS way
Author: Trenton Potgieter and Jonathan Dahlberg
Publisher finelybook 出版社: Packt Publishing (April 15, 2022)
Language 语言: English
Print Length 页数: 420 pages
ISBN-10: 1801811822
ISBN-13: 9781801811828
Book Description
By finelybook
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
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 Author: 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.
Author: 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
