Design a Recommendation Engine - AI-Powered Personalisation at Scale

Public lesson preview

Architect a production recommendation system combining collaborative filtering, embeddings, and real-time serving.

Learning objectives

By the end of this lesson, you will understand:

• ✓The Interview Prompt
• ✓Step 1: Requirements Gathering
• ✓Step 2: Recommendation Approaches
• ✓Step 3: The Recommendation Pipeline
• ✓Step 4: Data Model and Feature Store

Lesson outline

1. The Interview Prompt
2. Step 1: Requirements Gathering
3. Step 2: Recommendation Approaches
4. Step 3: The Recommendation Pipeline
5. Step 4: Data Model and Feature Store
6. Step 5: Real-Time vs Batch Recommendations
7. Step 6: A/B Testing Framework
8. Step 7: Cold-Start Solutions
9. Step 8: Scaling the System
10. Key Takeaways