AWS provides a wide range of solutions to help automate a machine learning (ML) workflow with just a few lines of code. With this practical book, you'll learn how to automate an ML pipeline using the various AWS services.
Automated Machine Learning on AWS begins with a quick overview of what the ML pipeline/process looks like and highlights the typical challenges you may face when building a pipeline. By reading the book, you'll become well versed in various AWS solutions, such as Amazon SageMaker Autopilot, AutoGluon, AWS Step Functions, and more, and will learn how 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 Amazon Managed Services for Apache Airflow and will build a managed Airflow environment. You'll also cover the key success criteria for an Machine Learning Software Development Life Cycle (MLSDLC) implementation and the process of creating a self-mutating CI/CD pipeline using the CDK from the perspective of the platform engineering team.
By the end of the book, you'll be able to effectively automate a complete ML pipeline and deploy it to production.
Who this book is for ?
- This book is for novice as well as experienced ML practitioners looking to automate the process of building, training, and deploying ML-based solutions into production, using both purpose-built and other AWS services. A basic understanding of the end-to-end ML process and concepts, Python programming, and AWS is necessary to make the most out of the book.
Section 1: Fundamentals of the Automated Machine Learning Process and AutoML on AWS
- This section will educate you on the complexities of the machine learning process, what AutoML is, and how it can be used to streamline the process.
This section comprises the following chapters: • Chapter 1, Getting Started with Automated Machine Learning on AWS • Chapter 2, Automating Machine Learning Model Development Using SageMaker Autopilot • Chapter 3, Automating Complicated Model Development with AutoGluon
Section 2: Automating the Machine Learning Process with Continuous Integration and Continuous Delivery (CI/CD)
- This section will introduce you to the concepts of CI/CD, and how they can be applied to the ML process, by combining both DevOps and MLOps methodologies. We will showcase the various AWS services that can be used to build and execute a CI/CD pipeline for the ML process. This section will walk you through how to construct the CI/CD pipeline as a cloud-native application using the Cloud Development Kit (CDK).
This section comprises the following chapters: • Chapter 4, Continuous Integration and Continuous Delivery (CI/CD) for Machine Learning • Chapter 5, Continuous Deployment of a Production ML Model
Section 3: Optimizing a Source Code-Centric Approach to Automated Machine Learning
This section will introduce you to the limitations of the overall CI/CD process and how to further integrate the role of the ML practitioner into the pipeline build process.
The section will also introduce how this role integration streamlines the automation process and present you with an optimized methodology by introducing you to AWS Step Functions.
This section comprises the following chapters: • Chapter 6, Automating the Machine Learning Process Using AWS Step Functions • Chapter 7, Building the ML Workflow Using AWS Step Functions
Section 4: Optimizing a Data-Centric Approach to Automated Machine Learning
- This section introduces you to what a data-centric ML process is, how it differs from a code-centric approach, and the services typically used for this methodology, namely, Apache Airflow and Amazon Managed Workflows for Apache Airflow.
This section comprises the following chapters: • Chapter 8, Automating the Machine Learning Process Using Apache Airflow • Chapter 9, Building the ML Workflow Using Amazon Managed Workflows for Apache Airflow
Section 5: Automating the End-to-End Production Application on AWS
This section will introduce you to the Machine Learning Software Development Life Cycle (MLSDLC) and how to implant the end-to-end process, with the ACME Fishing Logistics example. This section encompasses the various techniques learned in previous parts and shows how they fit into the MLSDLC.
The various chapters within this part will introduce you to what the MLSDLC is and how it works in practice by highlighting the various roles of a cross-functional team and how team members implement the CI(Continuous Integration), CD (Continuous Delivery), and CT (Continuous Training) aspects of the production application.
This section comprises the following chapters: • Chapter 10, An Introduction to the Machine Learning Software Development Life Cycle (MLSDLC) • Chapter 11, Continuous Integration, Deployment, and Training for the MLSDLC
Conclusion
- I thoroughly enjoyed reading "Automated Machine Learning on AWS" by Trenton Potgieter. Thank you to Shifa Ansari at Packt team for sharing this book and for the opportunity of an Editorial Review of the same. Strongly recommend this book if you’re looking to Fast-track the development of your production-ready machine learning applications the AWS way.
Personally, I loved 👀👇:
👉 The book starts by exploring different ML process and challenges faced when building a pipeline.
👉 the book does a good job of explaining how different AWS Services work together to automate Ml pipelines for Data Engineering Teams.
👉 Finally it helps us understand how to build, train and deploy ML-based solutions into production using AWS Services.
👉 Link of the Book is here
This post is a collaboration with Packt, I recommend following them if you are interested in book releases and growing the community! ❤