How to approach ML system design problem
Given the unstructured nature of ML system design questions, it’s crucial to approach them with a flexible yet structured framework. Start with a broad overview following a structured format, then be prepared to dive deep into specific areas based on the interviewer's focus. Here's how you can approach it:
1. Clarify Requirements
Understand the Business Objective: Begin by discussing the broader business context. What is the problem the system aims to solve?
Frame the ML Problem: Translate the business problem into an ML objective. For example, if the business wants to reduce churn, your ML problem might be to predict the likelihood of a user canceling a subscription.
Specify Inputs and Outputs: Clarify the type of data available (e.g., user behavior logs, transaction history) and what the desired output is (e.g., churn probability, recommendation list).
Determine the ML Category: Identify whether this is a classification, regression, clustering, or reinforcement learning problem.
2. Data Preparation
Data Engineering: Outline how data will be collected, cleaned, and transformed. Mention how you'll handle missing data, outliers, and ensure data consistency.
Feature Engineering: Discuss strategies for selecting, creating, or transforming features. Mention any domain-specific knowledge or automated techniques you might use.
3. Model Development
Model Exploration: Propose various models to experiment with, ranging from simple baselines to more complex architectures. Explain why you chose these models.
Training Strategy: Talk about how you’ll split the data, handle cross-validation, and optimize hyperparameters. Address potential issues like overfitting and how you’d mitigate them.
4. Evaluation
Offline Metrics: Select relevant evaluation metrics based on the problem (e.g., accuracy, precision, recall for classification; MAE, RMSE for regression).
Online Metrics: Discuss how you’d evaluate the model’s performance in production using A/B testing, and what online metrics are critical (e.g., click-through rate, conversion rate).
Bias and Fairness: Consider how to ensure the model is fair and unbiased, especially if it impacts diverse user groups.
5. Deployment and Serving
Deployment Strategy: Explain how you’ll deploy the model, considering factors like real-time vs. batch processing, scalability, and latency.
Monitoring and Maintenance: Discuss how you’ll monitor the model in production, detect drift, and plan for retraining.
6. Additional Talking Points
Interpretability: Talk about making the model's decisions understandable, especially if it’s a black-box model like a deep neural network.
Ethical and Privacy Considerations: Mention how you’d handle sensitive data and ensure the model adheres to ethical guidelines.
Scalability and Efficiency: Discuss strategies to ensure the system can handle increasing loads, like using distributed computing or cloud services.
7. Deep Dive
Component Focus: Be prepared to dive deeper into any component based on the interviewer's interest. For example, if they want more details on data pipelines, explain your approach to data engineering, or if they focus on model development, discuss advanced techniques like ensemble learning or transfer learning.
By starting with this structured approach, you can demonstrate a comprehensive understanding while being flexible enough to adapt to the specific interests of the interviewer.
Stay tuned for future posts where we will use above framework to approach ML system desing questions.