Design an ML pipeline for video recommendation
Last updated: July 5, 2025
Quick Overview
Design an end-to-end ML system for video recommendation, covering data collection, feature engineering, model selection, training, and serving.
Supabase
July 5, 202549
14
4,824 solved
Design an end-to-end ML system for video recommendation, covering data collection, feature engineering, model selection, training, and serving.
Machine learning questions at Supabase test both theoretical understanding and practical experience. This Phone Screen question evaluates your knowledge of ML fundamentals and your ability to apply them to real-world problems.
What the Interviewer Expects
- Explain the mathematical foundations with clarity
- Discuss practical implementation considerations and hyperparameter tuning
- Analyze the technique's strengths and weaknesses for different data types
- Demonstrate understanding of evaluation methodology and metrics
- Connect theory to real-world applications with concrete examples
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 ensure reproducibility in your ML pipeline?
- How would you explain this model's predictions to a non-technical stakeholder?
- How would you handle a highly imbalanced dataset?
- What are the computational costs of this approach at scale?
Sharpen Your Skills on Codemia
Practice similar problems with our interactive workspace, get AI feedback, and track your progress.
Explore ML Interview PrepSample Answer
Core Concept: Video Recommendation Systems
Video recommendation systems are a type of collaborative filtering that utilize user interaction data to predict which videos a user is likely to engage with. The core concept here is to leverage both...
How It Works: Mathematical Mechanisms
The recommendation pipeline can be structured using collaborative filtering via matrix factorization, where user-item interactions are represented as a sparse matrix. Each user and video can be embedd...
Submit Your Answer
