Design an ML pipeline for demand forecasting
Last updated: May 15, 2026
Quick Overview
Design an end-to-end ML system for demand forecasting, covering data collection, feature engineering, model selection, training, and serving.
Uber
May 15, 20262
5
3,508 solved
Design an end-to-end ML system for demand forecasting, covering data collection, feature engineering, model selection, training, and serving.
This ML question from Uber'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
- Explain the concept clearly with intuitive examples
- Discuss when and why to use this technique
- Identify common pitfalls and how to avoid them
- Compare with alternative approaches at a high level
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
- What are the computational costs of this approach at scale?
- How would you explain this model's predictions to a non-technical stakeholder?
- How would you ensure reproducibility in your ML pipeline?
- When would you prefer a simpler model over a complex one?
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Explore ML Interview PrepSample Answer
Core Concept: Demand Forecasting in ML
Demand forecasting involves predicting future demand for Uber's services in a specific area at a given time. This can be approached using time series forecasting, regression models, or deep learning t...
How It Works: Data Pipeline and Feature Engineering
The ML pipeline for demand forecasting begins with data collection from various sources: historical trip data, weather APIs, event calendars, and user activity logs. Feature engineering is crucial her...
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