Data-Centric Machine Learning with Python: The ultimate guide to engineering and deploying high-quality models based on good data

In the rapidly advancing data-driven world where data quality is pivotal to the success of machine learning and artificial intelligence projects, this critically timed guide provides a rare, end-to-end overview of data-centric machine learning (DCML), along with hands-on applications of technical and non-technical approaches to generating deeper and more accurate datasets.
This book will help you understand what data-centric ML/AI is and how it can help you to realize the potential of ‘small data’. Delving into the building blocks of data-centric ML/AI, you’ll explore the human aspects of data labeling, tackle ambiguity in labeling, and understand the role of synthetic data. From strategies to improve data collection to techniques for refining and augmenting datasets, you’ll learn everything you need to elevate your data-centric practices. Through applied examples and insights for overcoming challenges, you’ll get a roadmap for implementing data-centric ML/AI in diverse applications in Python.
By the end of this book, you’ll have developed a profound understanding of data-centric ML/AI and the proficiency to seamlessly integrate common data-centric approaches in the model development lifecycle to unlock the full potential of your machine learning projects by prioritizing data quality and reliability.

1143204539
Data-Centric Machine Learning with Python: The ultimate guide to engineering and deploying high-quality models based on good data

In the rapidly advancing data-driven world where data quality is pivotal to the success of machine learning and artificial intelligence projects, this critically timed guide provides a rare, end-to-end overview of data-centric machine learning (DCML), along with hands-on applications of technical and non-technical approaches to generating deeper and more accurate datasets.
This book will help you understand what data-centric ML/AI is and how it can help you to realize the potential of ‘small data’. Delving into the building blocks of data-centric ML/AI, you’ll explore the human aspects of data labeling, tackle ambiguity in labeling, and understand the role of synthetic data. From strategies to improve data collection to techniques for refining and augmenting datasets, you’ll learn everything you need to elevate your data-centric practices. Through applied examples and insights for overcoming challenges, you’ll get a roadmap for implementing data-centric ML/AI in diverse applications in Python.
By the end of this book, you’ll have developed a profound understanding of data-centric ML/AI and the proficiency to seamlessly integrate common data-centric approaches in the model development lifecycle to unlock the full potential of your machine learning projects by prioritizing data quality and reliability.

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Data-Centric Machine Learning with Python: The ultimate guide to engineering and deploying high-quality models based on good data

Data-Centric Machine Learning with Python: The ultimate guide to engineering and deploying high-quality models based on good data

Data-Centric Machine Learning with Python: The ultimate guide to engineering and deploying high-quality models based on good data
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Data-Centric Machine Learning with Python: The ultimate guide to engineering and deploying high-quality models based on good data

Data-Centric Machine Learning with Python: The ultimate guide to engineering and deploying high-quality models based on good data

Overview

In the rapidly advancing data-driven world where data quality is pivotal to the success of machine learning and artificial intelligence projects, this critically timed guide provides a rare, end-to-end overview of data-centric machine learning (DCML), along with hands-on applications of technical and non-technical approaches to generating deeper and more accurate datasets.
This book will help you understand what data-centric ML/AI is and how it can help you to realize the potential of ‘small data’. Delving into the building blocks of data-centric ML/AI, you’ll explore the human aspects of data labeling, tackle ambiguity in labeling, and understand the role of synthetic data. From strategies to improve data collection to techniques for refining and augmenting datasets, you’ll learn everything you need to elevate your data-centric practices. Through applied examples and insights for overcoming challenges, you’ll get a roadmap for implementing data-centric ML/AI in diverse applications in Python.
By the end of this book, you’ll have developed a profound understanding of data-centric ML/AI and the proficiency to seamlessly integrate common data-centric approaches in the model development lifecycle to unlock the full potential of your machine learning projects by prioritizing data quality and reliability.

Product Details

ISBN-13: 9781804612415
Publisher: Packt Publishing
Publication date: 02/29/2024
Sold by: Barnes & Noble
Format: eBook
Pages: 378
File size: 10 MB

About the Author

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.
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.
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.

Table of Contents

Table of Contents
  1. Exploring Data-Centric Machine Learning
  2. From Model-Centric to Data-Centric – ML's Evolution
  3. Principles of Data-Centric ML
  4. Data Labeling Is a Collaborative Process
  5. Techniques for Data Cleaning
  6. Techniques for Programmatic Labeling in Machine Learning
  7. Using Synthetic Data in Data-Centric Machine Learning
  8. Techniques for Identifying and Removing Bias
  9. Dealing with Edge Cases and Rare Events in Machine Learning
  10. Kick-Starting Your Journey in Data-Centric Machine Learning
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