Our team treats ML models like any other production service and follows a repeatable MLOps pipeline. Models are packaged in Docker images, versioned in a model registry (such as MLflow), and promoted through environments via CI/CD, with automated tests for data sanity, performance, and bias before release. We prefer canary or shadow deployments so new models see real traffic with minimal risk. For monitoring, we track both system metrics (latency, error rate, throughput) and ML metrics (feature distributions, data drift, prediction drift, and business KPIs) using a combination of centralized logging and tools like Prometheus and Grafana. Alerts trigger when metrics deviate from baselines, and scheduled retraining or rollback procedures are defined in advance, which has made running ML models in production far more stable and predictable.