Book description
Work seamlessly with production-ready machine learning systems and pipelines on AWS by addressing key pain points encountered in the ML life cycle
Key Features
- Gain practical knowledge of managing ML workloads on AWS using Amazon SageMaker, Amazon EKS, and more
- Use container and serverless services to solve a variety of ML engineering requirements
- Design, build, and secure automated MLOps pipelines and workflows on AWS
Book Description
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 machine learning book covers the essential concepts as well as step-by-step instructions that are designed to help you get a solid understanding of how to manage and secure ML workloads in the cloud. As you progress through the chapters, you'll discover how to use several container and serverless solutions when training and deploying TensorFlow and PyTorch deep learning models on AWS. You'll also delve into proven cost optimization techniques as well as data privacy and model privacy preservation strategies in detail as you explore best practices when using each AWS.
By the end of this AWS book, you'll be able to build, scale, and secure your own ML systems and pipelines, which will give you the experience and confidence needed to architect custom solutions using a variety of AWS services for ML engineering requirements.
What you will learn
- 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
Who this book is for
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.
Table of contents
- Machine Learning Engineering on AWS
- Copyright © 2022 Packt Publishing
- Contributors
- About the author
- About the reviewers
- Preface
- Part 1: Getting Started with Machine Learning Engineering on AWS
-
Chapter 1: Introduction to ML Engineering on AWS
- Technical requirements
- What is expected from ML engineers?
- How ML engineers can get the most out of AWS
- Essential prerequisites
- Preparing the dataset
- AutoML with AutoGluon
- Getting started with SageMaker and SageMaker Studio
- No-code machine learning with SageMaker Canvas
- AutoML with SageMaker Autopilot
- Summary
- Further reading
- Chapter 2: Deep Learning AMIs
- Chapter 3: Deep Learning Containers
- Part 2:Solving Data Engineering and Analysis Requirements
- Chapter 4: Serverless Data Management on AWS
- Chapter 5: Pragmatic Data Processing and Analysis
- Part 3: Diving Deeper with Relevant Model Training and Deployment Solutions
- Chapter 6: SageMaker Training and Debugging Solutions
-
Chapter 7: SageMaker Deployment Solutions
- Technical requirements
- Getting started with model deployments in SageMaker
- Preparing the pre-trained model artifacts
- Preparing the SageMaker script mode prerequisites
- Deploying a pre-trained model to a real-time inference endpoint
- Deploying a pre-trained model to a serverless inference endpoint
- Deploying a pre-trained model to an asynchronous inference endpoint
- Cleaning up
- Deployment strategies and best practices
- Summary
- Further reading
- Part 4:Securing, Monitoring, and Managing Machine Learning Systems and Environments
-
Chapter 8: Model Monitoring and Management Solutions
- Technical prerequisites
- Registering models to SageMaker Model Registry
- Deploying models from SageMaker Model Registry
- Enabling data capture and simulating predictions
- Scheduled monitoring with SageMaker Model Monitor
- Analyzing the captured data
- Deleting an endpoint with a monitoring schedule
- Cleaning up
- Summary
- Further reading
- Chapter 9: Security, Governance, and Compliance Strategies
- Part 5:Designing and Building End-to-end MLOps Pipelines
-
Chapter 10: Machine Learning Pipelines with Kubeflow on Amazon EKS
- Technical requirements
- Diving deeper into Kubeflow, Kubernetes, and EKS
- Preparing the essential prerequisites
- Setting up Kubeflow on Amazon EKS
- Running our first Kubeflow pipeline
- Using the Kubeflow Pipelines SDK to build ML workflows
- Cleaning up
- Recommended strategies and best practices
- Summary
- Further reading
-
Chapter 11: Machine Learning Pipelines with SageMaker Pipelines
- Technical requirements
- Diving deeper into SageMaker Pipelines
- Preparing the essential prerequisites
- Running our first pipeline with SageMaker Pipelines
- Creating Lambda functions for deployment
- Testing our ML inference endpoint
- Completing the end-to-end ML pipeline
- Cleaning up
- Recommended strategies and best practices
- Summary
- Further reading
- Index
- Other Books You May Enjoy
Product information
- Title: Machine Learning Engineering on AWS
- Author(s):
- Release date: October 2022
- Publisher(s): Packt Publishing
- ISBN: 9781803247595
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