Data Science

Model deployment strategies, monitoring and drift detection, CI/CD for ML models, feature store concepts, and model versioning

Data pipeline patterns, ETL/ELT best practices, data storage options, and data quality techniques

Model selection guidelines, feature engineering techniques, hyperparameter tuning strategies, evaluation metrics, and common ML frameworks

Statistical methods and tests, hypothesis testing, A/B testing frameworks, time series analysis, and experimental design