Machine Learning System Architecture Questions

Design and operational reasoning for end to end machine learning systems covering the full lifecycle from data sources to production serving and maintenance. Key areas include data ingestion and integration, storage choices such as data lakes and data warehouses, data validation cleaning and preprocessing, feature engineering and feature store design, experiment tracking and training infrastructure including distributed training and hyperparameter tuning, model validation evaluation explainability and fairness considerations, model packaging and model registry practices, deployment and serving architectures for batch online streaming and edge inference, monitoring and observability for data quality model performance and drift detection, feedback loops and automated retraining pipelines, model versioning rollback and controlled rollout strategies, and testing continuous integration and continuous delivery for models. Candidates should be able to explain data flow between components choose between batch and real time patterns reason about trade offs among latency throughput cost reliability and accuracy identify bottlenecks and failure modes propose mitigation strategies and name common architectural patterns operational practices and tooling used to build robust scalable and maintainable machine learning pipelines.