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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

Preparing the SageMaker script mode prerequisites

In this chapter, we will be preparing a custom script to use a pre-trained model for predictions. Before we can proceed with using the SageMaker Python SDK to deploy our pre-trained model to an inference endpoint, we’ll need to ensure that all the script mode prerequisites are ready.

Figure 7.4 – The desired file and folder structure

In Figure 7.4, we can see that there are three prerequisites we’ll need to prepare:

  • inference.py
  • requirements.txt
  • setup.py

We will store these prerequisites inside the scripts directory. We’ll discuss these prerequisites in detail in the succeeding pages of this chapter. Without further ado, let’s start by preparing the inference.py script file!

Preparing the inference.py file

In this section, we will prepare a custom Python script that will be used by SageMaker when processing inference requests. Here, we can influence...

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