Model Development Pipeline Questions

Covers the end to end process for developing predictive or analytical models in a software or data science context. Core stages include problem definition and success metrics, data discovery and collection, data labeling and annotation, data cleaning and preprocessing, exploratory analysis and feature engineering, model architecture selection and design, training approaches and hyperparameter tuning, validation and evaluation using appropriate metrics and cross validation, testing and robustness checks, deployment strategies, monitoring and observability in production, feedback loops and model iteration, data drift detection and retraining policies, and the engineering practices that enable repeatable delivery such as versioning, experiment tracking, and continuous integration and continuous deployment for models. The description applies across domains including natural language processing, computer vision, time series, and structured data.