Our team treats ML deployment like any other production software release. Models are packaged in Docker images with their dependencies pinned, and promoted through environments via a CI/CD pipeline (e.g., Jenkins/GitLab CI) triggered on versioned model and code changes. We use a model registry (such as MLflow) to track versions, metadata, and approval status, and rely on blue-green or canary releases to reduce risk during rollout. For monitoring, we capture both system metrics (latency, errors, throughput) and ML-specific signals (prediction distributions, data drift, model performance against delayed ground truth) using tools like Prometheus, Grafana, and logging/alerting pipelines. Regular retraining jobs and automated regression tests ensure new models only ship if they improve or maintain key business and quality KPIs.