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Full Description
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 DescriptionAWS 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 forThis 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.
Contents
Table of Contents
Getting Started with Automated Machine Learning on AWS
Automating Machine Learning Model Development Using SageMaker Autopilot
Automating Complicated Model Development with AutoGluon
Continuous Integration and Continuous Delivery (CI/CD) for Machine Learning
Continuous Deployment of a Production ML Model
Automating the Machine Learning Process Using AWS Step Functions
Building the ML Workflow Using AWS Step Functions
Automating the Machine Learning Process Using Apache Airflow
Building the ML Workflow Using Amazon Managed Workflows for Apache Airflow
An Introduction to the Machine Learning Software Development Lifecycle (MLSDLC)
Continuous Integration, Deployment, and Training for the MLSDLC
