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

By Jonas Christensen, Nakul Bajaj and 1 more

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BookEllipseFeb 2024Ellipse378 pagesEllipse1st Edition

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Key benefits

    • 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

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.

Who is this book 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.

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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Product Details


Publication date :

Feb 29, 2024

Length :

378 pages

Edition :

1st Edition

Language :

English

ISBN-13 :

9781804618127

Category :

Concepts :

Product Details


Publication date :

Feb 29, 2024

Length :

378 pages

Edition :

1st Edition

Language :

English

ISBN-13 :

9781804618127

Category :

Concepts :

What do you get with Print? Show details

Feature iconInstant access to your digital eBook copy whilst your Print order is Shipped
Feature iconBlack & white paperback book shipped to your address
Feature iconAI Assistant (beta) to help accelerate your development
Feature iconDownload this book in EPUB and PDF formats
Feature iconAccess this title in our online reader with advanced features
Feature iconDRM FREE - Read whenever, wherever and however you want

Estimated delivery fee

Location iconDeliver to

Premium
7 - 10 business days

undefined26.95

(Includes tracking information)

Product Details


Publication date :

Feb 29, 2024

Length :

378 pages

Edition :

1st Edition

Language :

English

ISBN-13 :

9781804618127

Category :

Concepts :

Table of Contents

17 Chapters


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Authors (3)

(1-1 of 3) 
Jonas Christensen
LinkedinGithub
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
LinkedinGithub
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
LinkedinGithub
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 abou...

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