Data-Centric Machine Learning with Python : The Ultimate Guide to Engineering and Deploying High-Quality Models Based on Good Data / Jonas Christensen, Nakul Bajaj and Manmohan Gosada ; foreword by Kirk D. Borne.

Format:

Book

Author/Creator:

Christensen, Jonas, author.

Bajaj, Nakul, author.

Gosada, Manmohan, author.

Contributor:

Borne, Kirk D., writer of foreword.

Language:

English

Subjects (All):

Machine learning.

Python (Computer program language).

Data mining.

Physical Description:

1 online resource (378 pages)

Edition:

First edition.

Place of Publication:

Birmingham, England : Packt Publishing, [2024]

Summary:

Join the data-centric revolution and master the concepts, techniques, and algorithms shaping the future of AI and ML development, using Python Key Features 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. 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 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.

Contents:

Cover

Title Page

Copyright and Credits

Foreword

Contributors

Table of Contents

Preface

Part 1: What Data-Centric Machine Learning Is and Why We Need It

Chapter 1: Exploring Data-Centric Machine Learning

Understanding data-centric ML

The origins of data centricity

The components of ML systems

Data is the foundational ingredient

Data-centric versus model-centric ML

Data centricity is a team sport

The importance of quality data in ML

Identifying high-value legal cases with natural language processing

Predicting cardiac arrests in emergency calls

Summary

References

Chapter 2: From Model-Centric to Data-Centric - ML's Evolution

Exploring why ML development ended up being mostly model-centric

The 1940s to 1970s - the early days

The 1980s to 1990s - the rise of personal computing and the internet

The 2000s - the rise of tech giants

2010-now - big data drives AI innovation

Model-centricity was the logical evolutionary outcome

Unlocking the opportunity for small data ML

Why we need data-centric AI more than ever

The cascading effects of data quality

Avoiding data cascades and technical debt

Part 2: The Building Blocks of Data-Centric ML

Chapter 3: Principles of Data-Centric ML

Sometimes, all you need is the right data

Principle 1 - data should be the center of ML development

A checklist for data-centricity

Principle 2 - leverage annotators and SMEs effectively

Direct labeling with human annotators

Verifying output quality with human annotators

Codifying labeling rules with programmatic labeling

Principle 3 - use ML to improve your data

Principle 4 - follow ethical, responsible, and well-governed ML practices

Chapter 4: Data Labeling Is a Collaborative Process.

Understanding the benefits of diverse human labeling

Understanding common challenges arising from human labelers

Designing a framework for high-quality labels

Designing clear instructions

Aligning motivations and using SMEs

Collaborating iteratively

Dealing with ambiguity and reflecting diversity

Understanding approaches for dealing with ambiguity in labeling

Measuring labeling consistency

Part 3: Technical Approaches to Better Data

Chapter 5: Techniques for Data Cleaning

The six key dimensions of data quality

Installing the required packages

Introducing the dataset

Ensuring the data is consistent

Checking that the data is unique

Ensuring that the data is complete and not missing

Ensuring that the data is valid

Ensuring that the data is accurate

Ensuring that the data is fresh

Chapter 6: Techniques for Programmatic Labeling in Machine Learning

Technical requirements

Python version

Library requirements

Pattern matching

Database lookup

Boolean flags

Weak supervision

Semi-weak supervision

Slicing functions

Active learning

Uncertainty sampling

Query by Committee (QBC)

Diversity sampling

Transfer learning

Feature extraction

Fine-tuning pre-trained models

Semi-supervised learning

Chapter 7: Using Synthetic Data in Data-Centric Machine Learning

Understanding synthetic data

The use case for synthetic data

Synthetic data for computer vision and image and video processing

Generating synthetic data using generative adversarial networks (GANs)

Exploring image augmentation with a practical example

Natural language processing

Privacy preservation

Generating synthetic data for privacy preservation

Using synthetic data to improve model performance.

When should you use synthetic data?

Chapter 8: Techniques for Identifying and Removing Bias

The bias conundrum

Types of bias

Easy to identify bias

Difficult to identify bias

The data-centric imperative

Sampling methods

Other data-centric techniques

Case study

Loading the libraries

AllKNN undersampling method

Instance hardness undersampling method

Oversampling methods

Shapley values to detect bias, oversample, and undersample data

Chapter 9: Dealing with Edge Cases and Rare Events in Machine Learning

Importance of detecting rare events and edge cases in machine learning

Statistical methods

Z-scores

Interquartile Range (IQR)

Box plots

Scatter plots

Anomaly detection

Unsupervised method using Isolation Forest

Semi-supervised methods using autoencoders

Supervised methods using SVMs

Data augmentation and resampling techniques

Oversampling using SMOTE

Undersampling using RandomUnderSampler

Cost-sensitive learning

Choosing evaluation metrics

Ensemble techniques

Bagging

Boosting

Stacking

Part 4: Getting Started with Data-Centric ML

Chapter 10: Kick-Starting Your Journey in Data-Centric Machine Learning

Solving six common ML challenges

Being a champion for data quality

Bringing people together

Taking accountability for AI ethics and fairness

Making data everyone's business - our own experience

Index

Other Books You May Enjoy.

Notes:

Includes bibliographical references and index.

Description based on publisher supplied metadata and other sources.

Description based on print version record.

ISBN:

9781804612415

1804612413

OCLC:

1423742058