Designing Machine Learning Systems | Chapter 4: Training Data

onepagecode · Beginner ·🔄 Data Engineering ·1w ago

About this lesson

Read the detailed version on: https://onepagecode.substack.com/ Use this url: https://onepagecode.substack.com/p/large-language-models-architectures In Chapter 4 of "Designing Machine Learning Systems" by Chip Huyen, we focus on one of the most critical yet often overlooked parts of building ML systems: creating high-quality training data. This chapter covers practical techniques for obtaining, sampling, labeling, and augmenting training data for production machine learning models. We start with different sampling strategies — including simple random sampling, stratified sampling, weighted sampling, reservoir sampling, and importance sampling — and discuss how to avoid common sampling biases. We then explore the challenges of labeling data, including hand-labeling difficulties, natural labels, label multiplicity, data lineage, and feedback loop length. The chapter also dives into powerful techniques for handling the lack of labeled data, such as weak supervision (Snorkel), semi-supervision, transfer learning, and active learning. Finally, we tackle the very common problem of class imbalance — why it makes learning difficult and how to handle it using the right evaluation metrics, resampling techniques, cost-sensitive learning, and focal loss. We also cover data augmentation methods for both computer vision and NLP. What you’ll learn in this chapter: • Different sampling methods and when to use each • Challenges with hand labeling and how to work with natural labels • Weak supervision and programmatic labeling with Snorkel • Semi-supervision, transfer learning, and active learning techniques • How to detect and handle class imbalance effectively • Cost-sensitive learning and focal loss • Data augmentation techniques (transformations, perturbation, mixup) • Why data lineage matters in production ML This chapter is essential for anyone building reliable ML systems where data quality directly impacts model performance. #DesigningMachineLearningSystems #ChipHuye

Original Description

Read the detailed version on: https://onepagecode.substack.com/ Use this url: https://onepagecode.substack.com/p/large-language-models-architectures In Chapter 4 of "Designing Machine Learning Systems" by Chip Huyen, we focus on one of the most critical yet often overlooked parts of building ML systems: creating high-quality training data. This chapter covers practical techniques for obtaining, sampling, labeling, and augmenting training data for production machine learning models. We start with different sampling strategies — including simple random sampling, stratified sampling, weighted sampling, reservoir sampling, and importance sampling — and discuss how to avoid common sampling biases. We then explore the challenges of labeling data, including hand-labeling difficulties, natural labels, label multiplicity, data lineage, and feedback loop length. The chapter also dives into powerful techniques for handling the lack of labeled data, such as weak supervision (Snorkel), semi-supervision, transfer learning, and active learning. Finally, we tackle the very common problem of class imbalance — why it makes learning difficult and how to handle it using the right evaluation metrics, resampling techniques, cost-sensitive learning, and focal loss. We also cover data augmentation methods for both computer vision and NLP. What you’ll learn in this chapter: • Different sampling methods and when to use each • Challenges with hand labeling and how to work with natural labels • Weak supervision and programmatic labeling with Snorkel • Semi-supervision, transfer learning, and active learning techniques • How to detect and handle class imbalance effectively • Cost-sensitive learning and focal loss • Data augmentation techniques (transformations, perturbation, mixup) • Why data lineage matters in production ML This chapter is essential for anyone building reliable ML systems where data quality directly impacts model performance. #DesigningMachineLearningSystems #ChipHuye

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