ML System Integration and Monitoring Questions

Consider how algorithmic and machine learning solutions integrate into production systems end to end. Coverage includes model and feature serving infrastructure, feature pipelines and feature stores, latency and throughput budgets, instrumentation and observability, metric design and alerting, model versioning and rollback, canary and shadow deployments, feedback loops between serving and data collection, trade offs between model complexity and operational constraints, capacity planning and cost control, and practices for ensuring reliability and debugging in production.