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Hate Speech and Harmful Content Detection at Scale

SYSTEM-DESIGNhardInterview freq: high
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🎯
Design a Scalable Hate Speech Detection System
45 min · senior
senior

You are tasked with designing a hate speech detection system for a social media platform with 100M DAU. The system must process 10,000 requests per second with a P99 latency SLA of 200ms. Your team of 5 engineers has 9 months to deliver a production-ready system. You have access to a labeled dataset of 1M comments, but the data is imbalanced with only 5% labeled as hate speech.

Given Info
  • 100M DAU with 10,000 requests per second
  • P99 latency SLA of 200ms
  • Team of 5 engineers, 9 months timeline
  • 1M labeled comments, 5% hate speech
  • Imbalanced dataset
📋
Guided Steps
1/4
Step 1
Data Preprocessing and Feature Engineering
How will you preprocess the data and engineer features to handle the imbalance and scale?
💡 Hint
Consider techniques for handling imbalanced data and the impact of feature complexity on latency.
Key Decision
Use SMOTE for data balancing and TF-IDF for feature extraction because they handle imbalance and scale efficiently.
Interviewer Expectation
Mid: Proposes basic text preprocessing and feature extraction. Senior: Also addresses data imbalance with techniques like SMOTE and justifies feature choice based on latency impact.
⚠️Common Wrong Answer
Ignoring data imbalance, leading to a model biased towards non-hate speech.
Step 2
Model Selection
Which model architecture will you choose and why?
💡 Hint
Think about the trade-off between model complexity and inference speed.
Key Decision
Choose a BERT-based model for its accuracy on text data, with optimizations for inference speed.
Interviewer Expectation
Mid: Suggests a simple model like logistic regression. Senior: Chooses BERT for accuracy and discusses optimizations like distillation or quantization for speed.
⚠️Common Wrong Answer
Selecting a complex model without considering inference latency, leading to SLA violations.
Step 3
Serving Architecture
How will you design the serving architecture to meet the latency requirements?
💡 Hint
Consider the trade-offs between batch and online processing.
Key Decision
Deploy a horizontally scalable microservices architecture with GPU acceleration for inference.
Interviewer Expectation
Mid: Proposes a basic serving setup. Senior: Designs a scalable architecture with GPU acceleration and discusses load balancing strategies.
⚠️Common Wrong Answer
Relying solely on CPU-based inference, resulting in latency issues under high load.
Step 4
Monitoring and Feedback Loop
How will you monitor the system and incorporate user feedback?
💡 Hint
Think about how to detect drift and gather user feedback effectively.
Key Decision
Implement real-time monitoring for drift and a feedback loop using user reports to retrain the model.
Interviewer Expectation
Mid: Mentions basic monitoring. Senior: Implements comprehensive monitoring for drift and a feedback loop for continuous improvement.
⚠️Common Wrong Answer
Neglecting drift monitoring, leading to model degradation over time.
📊
Scoring Rubric
▼ Show
Must Have - 60%
  • Addresses data imbalance with a specific technique like SMOTE.
  • Chooses a model architecture that balances accuracy and latency, such as BERT with optimizations.
  • Designs a scalable serving architecture with GPU acceleration.
  • Implements monitoring for drift and a feedback loop for model retraining.
Good to Have - 30%
  • Discusses feature extraction techniques like TF-IDF and their impact on latency.
  • Considers horizontal scaling and load balancing in the serving architecture.
  • Explains the trade-offs between batch and online processing.
  • Incorporates user feedback effectively into the retraining process.
Bonus - 10%
  • Proposes advanced model optimization techniques like distillation or quantization.
  • Designs a robust A/B testing framework for model updates.
  • Discusses the ethical implications of false positives and negatives in hate speech detection.
✅
Model Answer
▼ Reveal

The ideal solution involves using SMOTE to handle data imbalance and TF-IDF for feature extraction. A BERT-based model is chosen for its accuracy, with optimizations for inference speed. The serving architecture is a horizontally scalable microservices setup with GPU acceleration. Monitoring includes real-time drift detection and a feedback loop using user reports for continuous model improvement.

Key Decisions
  • Use SMOTE for data balancing to address the 5% hate speech imbalance.
  • Select a BERT-based model for its superior text classification performance, optimized for speed.
  • Deploy a microservices architecture with GPU acceleration to meet the 200ms latency SLA.
  • Implement real-time monitoring for drift and a feedback loop using user reports for retraining.
Anti-patterns
Ignoring data imbalance, leading to biased predictions.
Choosing a model without considering inference latency, causing SLA breaches.
Neglecting drift monitoring, resulting in model performance degradation.
Failing to incorporate user feedback, missing opportunities for model improvement.

Practice

(1/5)
1. In a production-scale embedding pipeline for text data, which component is responsible for converting raw text into numerical vectors?
easy
A. Data Loader
B. Feature Extractor
C. Tokenizer
D. Batch Normalizer

Solution

  1. Step 1: Identify the role of each component.

    The Tokenizer is responsible for converting raw text into tokens, which are then converted into numerical vectors.

  2. Step 2: Eliminate wrong options.

    Data Loader handles data input, Feature Extractor processes features but not directly from raw text, and Batch Normalizer normalizes data batches.

  3. Final Answer:

    Option C -> Option C

  4. Quick Check:

    Tokenizer converts text to tokens. [OK]
Hint: Tokenizer converts text to tokens. [OK]
Common Mistakes:
2. For a vision-language model designed to generate image captions, which metric should be prioritized to evaluate the quality of the generated captions?
easy
A. Accuracy
B. BLEU score
C. Precision
D. Recall

Solution

  1. Step 1: Understand the task requirements

    Image captioning requires evaluating the quality of generated text against reference captions.

  2. Step 2: Choose the appropriate metric

    BLEU score is specifically designed to evaluate the quality of text generation tasks like translation and captioning.

  3. Final Answer:

    Option B -> Option B

  4. Quick Check:

    BLEU evaluates text generation quality. [OK]
Hint: BLEU evaluates text generation quality. [OK]
Common Mistakes:
3. In the context of a Two-Tower Model for Retrieval, what is the primary role of the user tower?
easy
A. To generate item embeddings based on user interactions.
B. To generate user embeddings based on user features.
C. To compute similarity scores between users and items.
D. To directly predict user-item interaction scores.

Solution

  1. Step 1: Understand the user tower's role

    The user tower is responsible for generating embeddings that represent users based on their features.

  2. Step 2: Eliminate wrong options

    To generate item embeddings based on user interactions. is incorrect because item embeddings are generated by the item tower. To compute similarity scores between users and items. is incorrect because similarity scores are computed after embeddings are generated. To directly predict user-item interaction scores. is incorrect because the model predicts interaction scores using both towers' outputs.

  3. Final Answer:

    Option B -> Option B

  4. Quick Check:

    User tower generates user embeddings. [OK]
Hint: User tower generates user embeddings. [OK]
Common Mistakes:
4. In a recommendation system designed to avoid filter bubbles, what is a likely failure mode if the diversity re-ranking layer is not properly tuned?
medium
A. Loss of user engagement
B. Increased computational cost
C. Overfitting to user preferences
D. Increased latency in recommendations

Solution

  1. Step 1: Identify the impact of poor tuning

    Poor tuning can lead to irrelevant recommendations, reducing user engagement.

  2. Step 2: Eliminate wrong options

    Increased computational cost (A) and latency (D) are technical issues, not directly related to user engagement. Overfitting (C) is more about personalization than diversity.

  3. Final Answer:

    Option A -> Option A

  4. Quick Check:

    Poor tuning reduces engagement due to irrelevant content [OK]
Hint: Poor tuning reduces engagement due to irrelevant content [OK]
Common Mistakes:
5. As a news feed system scales to 10x more users, which component is most likely to become a bottleneck first?
hard
A. Content Delivery Network
B. Recommendation Engine
C. User Interface
D. Data Storage

Solution

  1. Step 1: Analyze scaling challenges

    The Recommendation Engine processes user data to generate personalized feeds, which becomes computationally intensive as user numbers grow.

  2. Step 2: Consider other components

    B: Content Delivery Network scales well with caching. C: User Interface is less affected by user count. D: Data Storage can be expanded more easily than processing power.

  3. Final Answer:

    Option B -> Option B

  4. Quick Check:

    Recommendation Engine struggles first with user scale. [OK]
Hint: Recommendation Engine struggles first with user scale. [OK]
Common Mistakes:
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