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Machine Learning Engineering on AWS
This is the code repository for Machine Learning Engineering on AWS, published by Packt.
Build, scale, and secure machine learning systems and MLOps pipelines in production
What is this book about?
There is a growing need for professionals with experience in working on machine learning (ML) engineering requirements as well as those with knowledge of automating complex MLOps pipelines in the cloud. This book explores a variety of AWS services, such as Amazon Elastic Kubernetes Service, AWS Glue, AWS Lambda, Amazon Redshift, and AWS Lake Formation, which ML practitioners can leverage to meet various data engineering and ML engineering requirements in production.
This book covers the following exciting topics:
- Find out how to train and deploy TensorFlow and PyTorch models on AWS
- Use containers and serverless services for ML engineering requirements
- Discover how to set up a serverless data warehouse and data lake on AWS
- Build automated end-to-end MLOps pipelines using a variety of services
- Use AWS Glue DataBrew and SageMaker Data Wrangler for data engineering
- Explore different solutions for deploying deep learning models on AWS
- Apply cost optimization techniques to ML environments and systems
- Preserve data privacy and model privacy using a variety of techniques
If you feel this book is for you, get your copy today!
Instructions and Navigations
All of the code is organized into folders. For example, chapter04.
The code will look like the following:
---
SELECT booking_changes, has_booking_changes, *
FROM dev.public.bookings
WHERE
(booking_changes=0 AND has_booking_changes='True')
OR
(booking_changes>0 AND has_booking_changes='False');
This book is for machine learning engineers, data scientists, and AWS cloud engineers interested in working on production data engineering, machine learning engineering, and MLOps requirements using a variety of AWS services such as Amazon EC2, Amazon Elastic Kubernetes Service (EKS), Amazon SageMaker, AWS Glue, Amazon Redshift, AWS Lake Formation, and AWS Lambda -- all you need is an AWS account to get started. Prior knowledge of AWS, machine learning, and the Python programming language will help you to grasp the concepts covered in this book more effectively.
With the following software and hardware list you can run all code files present in the book (Chapter 1-11).
Software and Hardware List
You will need an AWS account and a stable internet connection to complete the hands-on solutions in this book. If you still do not have an AWS account, feel free to check the AWS Free Tier page and click Create a Free Account: https://aws.amazon.com/free/.
| Chapter | Software required | OS required |
|---|---|---|
| 1-11 | Chrome, Firefox, Safari, Edge, Opera, or alternative | Windows, Mac OS X, and Linux (Any) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Get to Know the Author
Joshua Arvin Lat is the Chief Technology Officer (CTO) of NuWorks Interactive Labs, Inc. He previously served as the CTO of three Australian-owned companies and also served as the Director for Software Development and Engineering for multiple e-commerce start-ups in the past, which allowed him to be more effective as a leader. Years ago, he and his team won first place in a global cybersecurity competition with their published research paper. He is also an AWS Machine Learning Hero and has shared his knowledge at several international conferences, discussing practical strategies on machine learning, engineering, security, and management.
Download a free PDF
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.