MLflow models can be registered and then deployed using Azure ML, making direct deployment to Azure Machine Learning as a web service a valid method for production deployment. Exporting the model to a Docker container for deployment on Kubernetes is also a common and flexible production deployment approach. Manual deployment by downloading the model artifacts and using Azure Functions is possible but less automated and scalable. Uploading the model binaries directly to Azure SQL Database for real-time predictions is incorrect as Azure SQL Database is not designed to host or serve ML models directly.