Data-Centric Machine Learning with Python The Ultimate Guide to Engineering and Deploying High-Quality Models Based on Good Data
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 synth...
| Otros Autores: | , , , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England :
Packt Publishing
[2024]
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| Edición: | First edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009805127106719 |
Tabla de Contenidos:
- 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
- Summary
- References
- 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
- Summary
- References
- 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
- Summary
- References
- 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
- Summary
- 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
- Summary
- 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?
- Summary
- References
- 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
- Summary
- 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
- Summary
- 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
- Summary
- References
- Index
- Other Books You May Enjoy.
