Data

Data is the foundation of machine learning, and a thorough discussion of data is essential in any machine learning system design interview. Here are the core points from this lesson:

• Discuss labels, features, and data set splitting, understanding the trade-offs involved in each area
• Explore various methods for generating data labels, such as human annotation, synthetic data, and LLMs, while considering the pros and cons of each approach
• Focus on high-level feature sources, providing relevant examples and selecting predictive features while avoiding sensitive topics
• Understand feature encoding techniques, such as one-hot encoding and embeddings, and their impact on model performance
• Weigh the trade-offs between cross-validation and train-validate-test splits, while considering issues like data leakage and imbalances in the data set

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