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Spam Email Detection Pipeline
EasyML System Design
Design a machine learning system that classifies incoming emails as spam or not spam.
Scenario: An email provider wants to filter spam for millions of users. The system must process incoming emails in real time and handle evolving spam techniques.
Your Task: Implement a function that returns the system design specification as a structured dictionary. Your design should include:
- Data Pipeline - How to collect and preprocess email data
- Feature Engineering - What features to extract from emails
- Model - What ML model(s) to use for classification
- Serving - How to serve predictions in real time
- Monitoring - How to detect model degradation
Evaluation: Your design will be evaluated on completeness, appropriate model choices, and production readiness.
Design Mode
📝 Your Design Approach
Describe your system design approach. Consider components, data flow, and key decisions.
🎯 Design Questions(Select all that apply)
Q1.Which feature engineering approach is most effective for spam detection?
Q2.How should the system handle evolving spam techniques over time?
Q3.What is the most important metric to optimize for spam detection?
Q4.Which serving architecture best suits real-time email classification?
0 of 4 questions answered