ML Interview Questions

Design a comprehensive A/B test for a new feature in a machine learning system. Explain the steps you would take to ensure that the test is both statistically sound and practically applicable. Consider aspects such as sample size, duration, metrics to measure, and potential pitfalls.

Outline the architecture for an efficient image search system.

Design a scalable recommendation system for a large e-commerce platform. Discuss the architecture, key components, and how you would ensure it can handle millions of users and items. Consider both real-time and batch processing requirements.

Describe the different strategies for deploying machine learning models to production. Discuss the differences between batch processing and real-time processing in the context of ML model deployment. What are the considerations and trade-offs involved in choosing one over the other?

How do you handle feature engineering at scale in a production ML system? Discuss the strategies and tools you would employ to ensure that feature engineering is efficient, scalable, and maintainable.

How do you ensure fairness in machine learning systems, and what techniques can be used to detect and mitigate biases that may arise during model development and deployment?

Explain the ROC curve, AUC, and their significance in model evaluation.

Explain the differences between bagging and boosting in ensemble learning. Provide examples of algorithms that use each technique and discuss their respective advantages and potential drawbacks in terms of model performance and computational complexity.

Explain L1 and L2 regularization techniques and how they differ in terms of their impact on model parameters.

Explain the different types of gradient descent algorithms and their trade-offs, highlighting their theoretical background and practical applications.