ML Operations & Reliability at Large Scale Questions

Production ML systems lifecycle, including deployment, monitoring, scaling, and reliability practices for machine learning at large-scale platforms. Covers MLOps, model serving architectures, data quality and versioning, feature stores, canary rollouts, incident response, postmortems, and platform reliability considerations for ML workloads serving very high request volumes and large user bases.