This is a meaty one and I didn't structure my answer well.
Start by clarifying the problem constraints (dataset size, label scarcity, compute budget, latency requirements) and then propose a semi-supervised learning pipeline that leverages both labeled and unlabeled data. Discuss specific techniques like self-training, consistency regularization, and contrastive pre-training, and explain how to validate and iterate on the approach.
Pro tip: Emphasize the importance of a strong validation strategy and error analysis on the labeled set to avoid overfitting to noisy pseudo-labels, and mention that at Amazon, scalability and cost-efficiency are critical, so prefer methods that can be distributed and require minimal hyperparameter tuning.
Ask about dataset size, label ratio, compute resources, latency, and business metrics. Establish a baseline using only labeled data to quantify the gap.
Select between self-training, consistency regularization, or pre-training + fine-tuning based on data modality and constraints. Justify your choice with trade-offs.
Outline steps: pre-train on unlabeled data (if applicable), generate pseudo-labels with confidence thresholds, train with a combined loss, and iterate. Mention data augmentation and regularization.
Use a held-out labeled set for evaluation, monitor for confirmation bias, and employ techniques like co-training or ensemble to improve robustness.
Discuss distributed training, efficient data loading, and model serving considerations. Highlight monitoring and retraining strategies in production.
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