Join the data-centric revolution and master the concepts, techniques, and algorithms shaping the future of AI and ML development, using PythonKey FeaturesGrasp the principles of data centricity and apply them to real-world scenarios
Gain experience with quality data collection, labeling, and synthetic data creation using Python
Develop essential skills for building reliable, responsible, and ethical machine learning solutions
Purchase of the print or Kindle book includes a free PDF eBook

Book Description
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.What you will learnUnderstand the impact of input data quality compared to model selection and tuning
Recognize the crucial role of subject-matter experts in effective model development
Implement data cleaning, labeling, and augmentation best practices
Explore common synthetic data generation techniques and their applications
Apply synthetic data generation techniques using common Python packages
Detect and mitigate bias in a dataset using best-practice techniques
Understand the importance of reliability, responsibility, and ethical considerations in ML/AI

Who this book is for
This book is for data science professionals and machine learning enthusiasts looking to understand the concept of data-centricity, its benefits over a model-centric approach, and the practical application of a best-practice data-centric approach in their work. This book is also for other data professionals and senior leaders who want to explore the tools and techniques to improve data quality and create opportunities for small data ML/AI in their organizations.Table of ContentsExploring Data-Centric Machine Learning
From Model-Centric to Data-Centric - ML's Evolution
Principles of Data-Centric ML
Data Labeling Is a Collaborative Process
Techniques for Data Cleaning
Techniques for Programmatic Labeling in Machine Learning
Using Synthetic Data in Data-Centric Machine Learning
Techniques for Identifying and Removing Bias
Dealing with Edge Cases and Rare Events in Machine Learning
Kick-Starting Your Journey in Data-Centric Machine Learning