What is Text Classification and Why Does Scale Matter?

Definition
Text Classification assigns predefined categories to text documents. At scale, this means classifying millions to billions of documents with consistent accuracy, low latency, and manageable cost.
Why Scale Changes Everything
A classifier that works on 1000 documents might fail at 1 million. At small scale, you can use expensive models, tolerate slow inference, and manually review edge cases. At large scale, a model taking 500ms per document means 5.7 days to process 1 million documents. A model costing 0.01 dollars per call costs 10,000 dollars for 1 million documents. Manual review becomes impossible when 5% need human attention: 50,000 reviews.
Scale forces trade-offs. You might use a faster, cheaper model that is 3% less accurate. You might skip classification for low value documents. You might build tiered systems where cheap models handle easy cases and expensive models handle hard ones.
Common Use Cases
Content moderation classifies user content as safe, unsafe, or needing review. Sentiment analysis categorizes feedback as positive, negative, or neutral. Intent classification routes support tickets. Spam detection filters unwanted messages. Topic tagging organizes documents for search.
💡 Key Insight: Classification accuracy requirements vary by use case. Spam filtering at 99% still lets 1 in 100 spam through: annoying but tolerable. Medical triage at 99% means 1 in 100 urgent cases misclassified: potentially dangerous. Know your error tolerance before choosing a model.
The Scale Spectrum
Small scale (thousands): Any approach works. Medium scale (millions): Need efficient models and batching. Large scale (billions): Need distributed systems and aggressive optimization. Match architecture to scale.

💡 Key Takeaways

✓At scale, 500ms per document means 5.7 days for 1M documents. Latency that works at 1K fails at 1M.

✓Cost scales linearly: 0.01 dollars per call = 10,000 dollars for 1M classifications.

✓Manual review breaks at scale: 5% needing review = 50,000 human reviews at 1M scale.

✓Scale forces trade-offs: faster/cheaper models, tiered systems, skipping low value documents.

✓Error tolerance varies by use case: 99% is fine for spam but dangerous for medical triage.

📌 Interview Tips

1Quantify the scale problem: 500ms/doc × 1M docs = 5.7 days. Show you understand latency at scale.

2Mention tiered architectures early: cheap models for easy cases, expensive for hard ones.

3Ask about error tolerance first: spam at 99% is fine, medical triage at 99% is not.

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