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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

Downloading the sample dataset

In the succeeding sections of this chapter, we will work with a very simple synthetic dataset that contains only two columns – x and y. Here, x may represent an object’s relative position on the X-axis, while y may represent the same object’s position on the Y-axis. The following screenshot shows an example of what the data looks like:

Figure 2.20 – Sample dataset

ML is about finding patterns. With this dataset, we will build a model that tries to predict the value of y given the value of x later in this chapter. Once we’re able to build models with a simple example like this, it will be much easier to deal with more realistic datasets that contain more than two columns, similar to what we worked with in Chapter 1, Introduction to ML Engineering on AWS.

Note

In this book, we won’t limit ourselves to just tabular data and simple datasets. In Chapter 6, SageMaker Training and Debugging...

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