Production Readiness for Machine Learning Systems Questions

Describe how a research prototype is translated into a reliable production system. Discuss latency, throughput, scalability, memory and compute constraints, and techniques such as model quantization, batching, and caching. Cover robustness, monitoring, alerting, model drift detection, fallback strategies, and split testing strategies for incremental rollout. Explain trade offs between model accuracy and operational cost, privacy and regulatory constraints, and the design of retraining and deployment pipelines for maintainability and observability.