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Machine Learning Engineering on AWS

You're reading from   Machine Learning Engineering on AWS Build, scale, and secure machine learning systems and MLOps pipelines in production

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Product type Paperback
Published in Oct 2022
Publisher Packt
ISBN-13 9781803247595
Length 530 pages
Edition 1st Edition
Tools
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Author (1):
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Joshua Arvin Lat Joshua Arvin Lat
Author Profile Icon Joshua Arvin Lat
Joshua Arvin Lat
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Table of Contents (19) Chapters Close

Preface 1. Part 1: Getting Started with Machine Learning Engineering on AWS
2. Chapter 1: Introduction to ML Engineering on AWS FREE CHAPTER 3. Chapter 2: Deep Learning AMIs 4. Chapter 3: Deep Learning Containers 5. Part 2:Solving Data Engineering and Analysis Requirements
6. Chapter 4: Serverless Data Management on AWS 7. Chapter 5: Pragmatic Data Processing and Analysis 8. Part 3: Diving Deeper with Relevant Model Training and Deployment Solutions
9. Chapter 6: SageMaker Training and Debugging Solutions 10. Chapter 7: SageMaker Deployment Solutions 11. Part 4:Securing, Monitoring, and Managing Machine Learning Systems and Environments
12. Chapter 8: Model Monitoring and Management Solutions 13. Chapter 9: Security, Governance, and Compliance Strategies 14. Part 5:Designing and Building End-to-end MLOps Pipelines
15. Chapter 10: Machine Learning Pipelines with Kubeflow on Amazon EKS 16. Chapter 11: Machine Learning Pipelines with SageMaker Pipelines 17. Index 18. Other Books You May Enjoy

Getting started with the SageMaker Python SDK

The SageMaker Python SDK is a library that allows ML practitioners to train and deploy ML models using the different features and capabilities of SageMaker. It provides several high-level abstractions such as Estimators, Models, Predictors, Sessions, Transformers, and Processors, all of which encapsulate and map to specific ML processes and entities. These abstractions allow data scientists and ML engineers to manage ML experiments and deployments with just a few lines of code. At the same time, infrastructure management is handled by SageMaker already, so all we need to do is configure these high-level abstractions with the correct set of parameters.

Note that it is also possible to use the different capabilities and features of SageMaker using the boto3 library. Compared to using the SageMaker Python SDK, we would be working with significantly more lines of code with boto3 since we would have to take care of the little details when...

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