Grasp 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
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 learn
Understand 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
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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.
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.
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.
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