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Design ML System: Duplicate Detection

Problem Statement

What is Near-Duplicate Detection?

Near-duplicate detection identifies content that is semantically similar but not identical. Unlike exact matching, it uses ML to find variations, rephrases, or slightly modified versions of content. This is crucial for data quality in ML pipelines where duplicate examples can bias model training and reduce dataset value.

Your Task: Design an ML system for Scale AI that detects near-duplicate content in datasets. The system should handle both text (prompts/responses) and images, providing real-time duplicate detection when new items are added to datasets. Focus on the complete ML pipeline from ingestion to feedback learning.

Duration:60 minutes
Difficulty:Advanced
Focus:ML System Design
Company:Scale AI

Q: What types of content are we detecting duplicates for?

A: Primarily text data (prompts/responses) and images. Text data includes customer datasets with prompts and responses for generative AI training. Images include vision labeling datasets.

Analysis: This affects our choice of embedding models and similarity metrics. We'll need separate pipelines for text and image content.

Q: What constitutes a 'near-duplicate'? How similar is too similar?

A: Near-duplicates are items that are semantically similar but not identical. For text: similar meaning/intent but different wording (e.g., 'How to cook pasta?' vs 'What's the best way to make pasta?'). For images: visually similar but potentially different angles, lighting, or minor variations.

Analysis: We need configurable similarity thresholds based on use case. Different domains may have different tolerance levels.

Q: What's the expected scale - how many items per dataset and how many datasets?

A: Individual datasets range from 1,000 to 1 million items. We handle hundreds of active datasets simultaneously. New items are added continuously throughout the day.

Analysis: This scale requires efficient indexing strategies and distributed processing. We'll need to partition by dataset and use approximate nearest neighbor search.

Q: What's the performance requirement for duplicate detection?

A: Near real-time detection under 1 second 95% of the time when adding new items. The candidate pool update can happen every few hours, and model updates weekly.

Analysis: Real-time requirements suggest we need a serving layer with pre-computed embeddings and fast similarity search infrastructure.

Q: How do we handle the continuous learning aspect?

A: We receive a stream of human-verified positive and negative pairs. These should improve our similarity thresholds and potentially fine-tune our models over time.

Analysis: We need a feedback loop system that can adapt thresholds dynamically and potentially retrain models with new labeled data.

Q: What are the consequences of false positives vs false negatives?

A: False positives (flagging unique content as duplicate) waste human reviewer time but prevent low-quality datasets. False negatives (missing actual duplicates) reduce dataset quality and may disappoint customers.

Analysis: We should err on the side of false positives for data quality, but need configurable precision/recall trade-offs per customer.

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🎯 Interview Practice Questions

Practice these follow-up questions to demonstrate deep understanding of ML duplicate detection systems in interviews.

1. Embedding Model Selection Strategy

"Your duplicate detection system needs to handle both short social media posts and long-form articles. How do you design an embedding strategy that captures semantic similarity across different content lengths while maintaining consistent similarity thresholds?"

2. Real-time Duplicate Prevention

"Design a system that prevents duplicate content submission in real-time during content creation. How do you handle partial matches, typing delays, and ensure <200ms response time while maintaining 99.9% accuracy for 10M+ content submissions per day?"

3. Multi-Modal Duplicate Detection

"Extend your system to detect duplicates across text, images, and videos. How do you design unified similarity scoring, handle cross-modal duplicates (same video with different titles), and balance computational costs across different media types?"

4. Threshold Optimization and Feedback Loop

"Your system has 85% precision but reviewers complain about false positives. Design an adaptive threshold system that learns from human feedback, handles concept drift, and automatically adjusts similarity thresholds per content category while maintaining audit logs."

5. Scalable Vector Search Architecture

"Your embedding database grows from 10M to 1B vectors. Design a sharding and indexing strategy for HNSW that maintains sub-second search latency, handles hot-partitioning, and supports incremental index updates without service interruption."

6. Cross-Language and Localization Challenges

"Design duplicate detection for a global platform supporting 50+ languages. How do you handle cross-language duplicates (same content in different languages), cultural context variations, and ensure consistent quality across different language embeddings?"