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
- 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
Trenton Potgieter is a Sr. AI/ML Specialist at AWS and has been working in the field of machine learning since 2011. At AWS, he assists multiple AWS customers to create ML solutions and has contributed to various use cases broadly spanning computer vision, knowledge graphs, and ML automation using MLOps methodologies. Trenton plays a key role in evangelizing the AWS ML services and shares best practices through forums such as AWS blogs, whitepapers, reference architectures, and public-speaking events. He has also actively been involved in leading, developing, and supporting an AWS internal community of MLOps-related subject matter experts.
