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Data-Centric Machine Learning with Python

Name: Data-Centric Machine Learning with Python
ISBN: 9781804618127

by Jonas Christensen, Nakul Bajaj, Manmohan Gosada

February 2024

Intermediate to advanced

378 pages

10h 10m

English

Packt Publishing

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Data-Centric Machine Learning with Python
ForewordContributorsAbout the authorsAbout the reviewers
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesConventions usedGet in touchShare Your ThoughtsDownload a free PDF copy of this book
Part 1: What Data-Centric Machine Learning Is and Why We Need It
Chapter 1: Exploring Data-Centric Machine Learning
Understanding data-centric MLThe origins of data centricityThe components of ML systemsData is the foundational ingredientData-centric versus model-centric MLData centricity is a team sportThe importance of quality data in MLIdentifying high-value legal cases with natural language processingPredicting cardiac arrests in emergency callsSummaryReferences
Chapter 2: From Model-Centric to Data-Centric – ML’s Evolution
Exploring why ML development ended up being mostly model-centricThe 1940s to 1970s – the early daysThe 1980s to 1990s – the rise of personal computing and the internetThe 2000s – the rise of tech giants2010–now – big data drives AI innovationModel-centricity was the logical evolutionary outcomeUnlocking the opportunity for small data MLWhy we need data-centric AI more than everThe cascading effects of data qualityAvoiding data cascades and technical debtSummaryReferences
Part 2: The Building Blocks of Data-Centric ML
Chapter 3: Principles of Data-Centric ML
Sometimes, all you need is the right dataPrinciple 1 – data should be the center of ML developmentA checklist for data-centricityPrinciple 2 – leverage annotators and SMEs effectivelyDirect labeling with human annotatorsVerifying output quality with human annotatorsCodifying labeling rules with programmatic labelingPrinciple 3 – use ML to improve your dataPrinciple 4 – follow ethical, responsible, and well-governed ML practicesSummaryReferences
Chapter 4: Data Labeling Is a Collaborative Process
Understanding the benefits of diverse human labelingUnderstanding common challenges arising from human labelersDesigning a framework for high-quality labelsDesigning clear instructionsAligning motivations and using SMEsCollaborating iterativelyDealing with ambiguity and reflecting diversityUnderstanding approaches for dealing with ambiguity in labelingMeasuring labeling consistencySummaryReferences
Part 3: Technical Approaches to Better Data
Chapter 5: Techniques for Data Cleaning
The six key dimensions of data qualityInstalling the required packagesIntroducing the datasetEnsuring the data is consistentChecking that the data is uniqueEnsuring that the data is complete and not missingEnsuring that the data is validEnsuring that the data is accurateEnsuring that the data is freshSummary

Chapter 6: Techniques for Programmatic Labeling in Machine Learning
Technical requirementsPython versionLibrary requirementsPattern matchingDatabase lookupBoolean flagsWeak supervisionSemi-weak supervisionSlicing functionsActive learningUncertainty samplingQuery by Committee (QBC)Diversity samplingTransfer learningFeature extractionFine-tuning pre-trained modelsSemi-supervised learningSummary
Chapter 7: Using Synthetic Data in Data-Centric Machine Learning
Understanding synthetic dataThe use case for synthetic dataSynthetic data for computer vision and image and video processingGenerating synthetic data using generative adversarial networks (GANs)Exploring image augmentation with a practical exampleNatural language processingPrivacy preservationGenerating synthetic data for privacy preservationUsing synthetic data to improve model performanceWhen should you use synthetic data?SummaryReferences
Chapter 8: Techniques for Identifying and Removing Bias
The bias conundrumTypes of biasEasy to identify biasDifficult to identify biasThe data-centric imperativeSampling methodsOther data-centric techniquesCase studyLoading the librariesAllKNN undersampling methodInstance hardness undersampling methodOversampling methodsShapley values to detect bias, oversample, and undersample dataSummary
Chapter 9: Dealing with Edge Cases and Rare Events in Machine Learning
Importance of detecting rare events and edge cases in machine learningStatistical methodsZ-scoresInterquartile Range (IQR)Box plotsScatter plotsAnomaly detectionUnsupervised method using Isolation ForestSemi-supervised methods using autoencodersSupervised methods using SVMsData augmentation and resampling techniquesOversampling using SMOTEUndersampling using RandomUnderSamplerCost-sensitive learningChoosing evaluation metricsEnsemble techniquesBaggingBoostingStackingSummary
Part 4: Getting Started with Data-Centric ML
Chapter 10: Kick-Starting Your Journey in Data-Centric Machine Learning
Solving six common ML challengesBeing a champion for data qualityBringing people togetherTaking accountability for AI ethics and fairnessMaking data everyone’s business – our own experienceSummaryReferences
Index
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Content preview from Data-Centric Machine Learning with Python

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

In this part, we take a deep dive into data-centric machine learning, contrasting it with model-centric approaches. We use real-life examples to illustrate their differences and explore the evolution of AI and ML toward a data-centric perspective. We also dispel the myth of “big data,” highlighting the importance of quality over quantity, and the potential for democratizing ML solutions. Prepare for a fresh perspective on the transformative power of data in ML.

This part has the following chapters:

Chapter 1, Exploring Data-Centric Machine Learning
Chapter 2, From Model-Centric to Data-Centric – ML’s Evolution

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Data Labeling in Machine Learning with Python

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Data-Centric Machine Learning with Python

by Jonas Christensen, Nakul Bajaj, Manmohan Gosada

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

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.