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AWS FinOps Simplified

You're reading from   AWS FinOps Simplified Eliminate cloud waste through practical FinOps

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Product type Paperback
Published in Oct 2022
Publisher Packt
ISBN-13 9781803247236
Length 292 pages
Edition 1st Edition
Tools
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Author (1):
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Peter Chung Peter Chung
Author Profile Icon Peter Chung
Peter Chung
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Toc

Table of Contents (18) Chapters Close

Preface 1. Chapter 1: FinOps Foundation 2. Part 1: Managing Your AWS Inventory FREE CHAPTER
3. Chapter 2: Establishing the Right Account Structure 4. Chapter 3: Managing Inventory 5. Chapter 4: Planning and Metrics Tracking 6. Chapter 5: Governing Cost and Usage 7. Part 2: Optimizing Your AWS Resources
8. Chapter 6: Optimizing Compute 9. Chapter 7: Optimizing Storage 10. Chapter 8: Optimizing Networking 11. Chapter 9: Optimizing Cloud-Native Environments 12. Part 3: Operationalizing FinOps
13. Chapter 10: Data-Driven FinOps 14. Chapter 11: Driving FinOps Autonomously 15. Chapter 12: Management Functions 16. Index 17. Other Books You May Enjoy

Optimizing ML

To uncover how we can optimize our ML costs, we must first understand which tasks constitute an ML workflow. We’ll look at the various steps involved in a typical ML process. Then, we’ll apply optimization methods to those specific steps using the various capabilities in AWS. We’ll focus on how you can optimize your model-training costs and model-deployment costs with Amazon SageMaker.

Understanding an ML workflow

An ML workflow typically requires data exploration and then feature engineering (FE) to transfer data to a format that can be used by an ML algorithm. The algorithm reads the data to find patterns and learns in a sense to generalize patterns so that it can predict outcomes on new, or unknown, data. This is often referred to as model training—you’re applying some mathematical algorithm that may be known and used popularly or something you created yourself to data that is proprietary to you or your organization. The application...

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