Code: USD75OFF
This is the code repository for Automated Machine Learning on AWS, published by Packt.
Fast-track the development of your production-ready machine learning applications the AWS way
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.
This book covers the following exciting features:
- 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
For supplemental content that covers Generative AI on AWS, as well as updates to AWS capabilities, such as SageMaker Pipelines, and advanced features for production ML model monitoring, take a look at www.automatedmlonaws.com.
If you feel this book is for you, get your copy today!
All of the code is organized into folders.
The code will look like the following:
import boto3
import sagemaker
aws_region = sagemaker.Session().boto_session.region_name
!sm-docker build --build-arg REGION={aws_region}
Following is what you need for this book: 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. .
With the following software and hardware list you can run all code files present in the book (Chapter 1-11).
| Chapter | Software required | OS required |
|---|---|---|
| 1-11 | Python 3.7.10 (and above) | Windows, Mac OS X, and Linux (Any) |
| 1-11 | AWS CLI 1.19.112 (and above) | Windows, Mac OS X, and Linux (Any) |
| 1-11 | AWS CDK 2.3.0 (build beaa5b2) | Windows, Mac OS X, and Linux (Any) |
It is recommended that you use an AWS Cloud9 integrated development environment as it meets the software/hardware and operating system requirements
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Trenton Potgieter is a senior AI/ML specialist at AWS and has been working in the field of ML 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 internal AWS community of MLOps-related subject matter experts.
If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.