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

Product type Paperback

Published in Oct 2022

Publisher Packt

ISBN-13 9781803247595

Length 530 pages

Edition 1st Edition

Tools

AWS

Concepts

Machine Learning

Author (1):

Joshua Arvin Lat

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Table of Contents (19) Chapters

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

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18. Other Books You May Enjoy

Serverless ML deployment with Lambda’s container image support

Now that we have the model.pth file, what do we do with it? The answer is simple: we will deploy this model in a serverless API using an AWS Lambda function and an Amazon API Gateway HTTP API, as shown in the following diagram:

Figure 3.11 – Serverless ML deployment with an API Gateway and AWS Lambda

As we can see, the HTTP API should be able to accept GET requests from “clients” such as mobile apps and other web servers that interface with end users. These requests then get passed to the AWS Lambda function as input event data. The Lambda function then loads the model from the model.pth file and uses it to compute the predicted y value using the x value from the input event data.

Building the custom container image

Our AWS Lambda function code needs to utilize PyTorch functions and utilities to load the model. To get this setup working properly, we will build...

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

Table of Contents (19) Chapters

Serverless ML deployment with Lambda’s container image support

Building the custom container image

Authors (1)

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

Table of Contents (19) Chapters

Serverless ML deployment with Lambda’s container image support

Building the custom container image

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Authors (1)

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