Design an ML pipeline for video recommendation
Last updated: February 16, 2026
Quick Overview
Design an end-to-end ML system for video recommendation, covering data collection, feature engineering, model selection, training, and serving.
Morgan Stanley
February 16, 20268
6
3,939 solved
Design an end-to-end ML system for video recommendation, covering data collection, feature engineering, model selection, training, and serving.
This ML question from Morgan Stanley's Onsite goes beyond textbook definitions. The interviewer wants to see how you reason about model selection, evaluation metrics, and the practical challenges of deploying ML in production.
What the Interviewer Expects
- Derive key equations and explain the optimization process in depth
- Discuss state-of-the-art variations and recent research developments
- Analyze computational complexity and scalability
- Implement core components from scratch with clean code
- Discuss production deployment challenges and solutions
- Compare with cutting-edge alternatives and justify your recommendation
Key Topics to Cover
How to Approach This
- Understand the bias-variance trade-off. High training accuracy but low test accuracy signals overfitting.
- Choose evaluation metrics carefully based on the problem. Accuracy alone is often insufficient.
- Feature engineering is often more impactful than model selection.
- Know when to use tree-based models (tabular data) vs neural networks (unstructured data).
- Handle class imbalance with SMOTE, class weights, or appropriate loss functions.
Possible Follow-up Questions
- How would you explain this model's predictions to a non-technical stakeholder?
- What regularization technique would you use and why?
- What are the computational costs of this approach at scale?
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Explore ML Interview PrepSample Answer
Core Concept: Video Recommendation Systems
Video recommendation systems typically utilize collaborative filtering and content-based filtering techniques, often enhanced through ensemble methods. Collaborative filtering relies on user behavior ...
How It Works: The Mathematical Mechanism
In a video recommendation pipeline, the mathematical foundation often involves matrix factorization techniques, such as Singular Value Decomposition (SVD). For example, we can represent user preferenc...
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