Ciklum Machine Learning Engineer Interview Questions
The questions to prepare for a Ciklum Machine Learning Engineer interview. Questions from real interview reports rank first. Updated daily.
Explain what causes overfitting and underfitting in deep learning, how to spot each one, and how to reduce them in practice.
Build a classifier for a highly imbalanced dataset and choose metrics, sampling, and thresholds that fit the minority class.
Explain how bias and variance affect generalization, and how model complexity changes the balance.
Explain how bagging and boosting differ, and identify a representative algorithm for each ensemble method.
Design a reliable path from trained ML models to monitored, scalable production inference.
Design a reproducible CI/CD pipeline that tests, packages, deploys, promotes, and rolls back machine learning workflows.
Approach for continuously monitoring a deployed model and keeping performance stable as data changes.
Best practices for reproducible dataset and model versioning in shared ML pipelines.
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