Production Machine Learning Systems Questions

Design, build, deploy, and operate end to end machine learning systems in production. Topics include data ingestion and validation, feature engineering and real time feature computation, training and testing pipelines, model serving and prediction latency optimization, scalability and reliability of infrastructure, and monitoring and observability for data and model performance. Covers detection and handling of data drift and model drift, retraining strategies and automation, versioning and reproducibility for data code and models, experiment tracking and model registries, and practices for continuous integration and continuous delivery in machine learning contexts. At senior and staff levels, expect system level trade offs, designing platform capabilities for multiple teams, debugging production performance regressions, and managing technical debt in machine learning systems.