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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Spis treści

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

• 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