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Data-Centric Machine Learning with Python

You're reading from Data-Centric Machine Learning with Python

Product type Book

Published in Feb 2024

Publisher Packt

ISBN-13 9781804618127

Pages 378 pages

Edition 1st Edition

Languages

Concepts

Authors (3):

Jonas Christensen

Nakul Bajaj

Manmohan Gosada

View More author details

Table of Contents (17) Chapters

Preface

1. Part 1: What Data-Centric Machine Learning Is and Why We Need It

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

Why subscribe?

16. Other Books You May Enjoy

Why we need data-centric AI more than ever

The leading organizations in AI, such as the Big Nine, have achieved incredible results with ML since the turn of the century, but how is AI being used in the long tail?

A 2020 survey published by MIT Sloan Management Review and Boston Consulting Group concluded that most companies struggle to turn their vision for AI into reality. In a survey of over 3,000 business leaders from 29 industries in 112 countries, 70% of respondents understood how AI can generate business value and 57% had piloted or productionized AI solutions. However, only 1 in 10 had been able to generate significant financial benefits with AI.20

The survey authors found that companies that were realizing significant financial benefits with AI had built their success on two pillars:

They had a solid foundation of the right data, technology, and talent.
They had defined several effective ways for humans and AI to work and learn together. In other words, they...

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Authors (3)

Jonas Christensen

Jonas Christensen has spent his career leading data science functions across multiple industries. He is an international keynote speaker, postgraduate educator, and advisor in the fields of data science, analytics leadership, and machine learning and host of the Leaders of Analytics podcast.

See other products by Jonas Christensen

Nakul Bajaj

Nakul Bajaj is a data scientist, MLOps engineer, educator and mentor, helping students and junior engineers navigate their data journey. He has a strong passion for MLOps, with a focus on reducing complexity and delivering value from machine learning use-cases in business and healthcare.

See other products by Nakul Bajaj

Manmohan Gosada

Manmohan Gosada is a seasoned professional with a proven track record in the dynamic field of data science. With a comprehensive background spanning various data science functions and industries, Manmohan has emerged as a leader in driving innovation and delivering impactful solutions. He has successfully led large-scale data science projects, leveraging cutting-edge technologies to implement transformative products. With a postgraduate degree, he is not only well-versed in the theoretical foundations of data science but is also passionate about sharing insights and knowledge. A captivating speaker, he engages audiences with a blend of expertise and enthusiasm, demystifying complex concepts in the world of data science.

Read more

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