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Data-Centric Machine Learning with Python

You're reading from   Data-Centric Machine Learning with Python The ultimate guide to engineering and deploying high-quality models based on good data

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Product type Paperback
Published in Feb 2024
Publisher Packt
ISBN-13 9781804618127
Length 378 pages
Edition 1st Edition
Languages
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Authors (3):
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Jonas Christensen Jonas Christensen
Author Profile Icon Jonas Christensen
Jonas Christensen
Manmohan Gosada Manmohan Gosada
Author Profile Icon Manmohan Gosada
Manmohan Gosada
Nakul Bajaj Nakul Bajaj
Author Profile Icon Nakul Bajaj
Nakul Bajaj
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Table of Contents (17) Chapters Close

Preface 1. Part 1: What Data-Centric Machine Learning Is and Why We Need It FREE CHAPTER
2. Chapter 1: Exploring Data-Centric Machine Learning 3. Chapter 2: From Model-Centric to Data-Centric – ML’s Evolution 4. Part 2: The Building Blocks of Data-Centric ML
5. Chapter 3: Principles of Data-Centric ML 6. Chapter 4: Data Labeling Is a Collaborative Process 7. Part 3: Technical Approaches to Better Data
8. Chapter 5: Techniques for Data Cleaning 9. Chapter 6: Techniques for Programmatic Labeling in Machine Learning 10. Chapter 7: Using Synthetic Data in Data-Centric Machine Learning 11. Chapter 8: Techniques for Identifying and Removing Bias 12. Chapter 9: Dealing with Edge Cases and Rare Events in Machine Learning 13. Part 4: Getting Started with Data-Centric ML
14. Chapter 10: Kick-Starting Your Journey in Data-Centric Machine Learning 15. Index 16. Other Books You May Enjoy

Unlocking the opportunity for small data ML

The group of tech companies famously labeled The Big Nine by author Amy Webb18 are examples of consumer internet companies that have leveraged big data and AI to build world dominance. Amazon, Apple, Alibaba, Baidu, Meta, Google, IBM, Microsoft, and Tencent dominate in the digital era because they utilize enormous amounts of user data to power their AI systems.

As network-based AI-first businesses, they have amassed customers on an unprecedented scale because users are happy to co-create and share their data, so long as it is a net benefit to them. For the Big Nine, getting enough modeling data is rarely a problem, and investing in the most advanced ML capabilities is a virtuous circle that enables more market dominance.

For most other organizations – and ML use cases – this sort of scale is unachievable. As we explored in Chapter 1, Exploring Data-Centric Machine Learning the long tail of ML opportunities doesn’...

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