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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
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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

Understanding how AWS pricing works for EC2 instances

Before we end this chapter, we must have a good idea of how AWS pricing works when dealing with EC2 instances. We also need to understand how the architecture and setup affect the overall cost of running ML workloads in the cloud.

Let’s say that we initially have a single p2.xlarge instance running 24/7 for an entire month in the Oregon region. Inside this instance, the data science team regularly runs a script that trains a deep learning model using the preferred ML framework. This training script generally runs for about 3 hours twice every week. Given the unpredictable schedule of the availability of new data, it’s hard to know when the training script will be run to produce a new model. The resulting ML model then gets deployed immediately to a web API server, which serves as the inference endpoint within the same instance. Given this information, how much would the setup cost?

Figure 2...

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