Model Deployment in a MLOps Workflow: The Various Ways
In MLOps pipelines, deployment is the pivotal phase where machine learning models transform from development artifacts into production-ready assets. The MLOps Zoomcamp Module 4: Deployment outlines three primary deployment strategies: 1. Web-services: Flask + Docker 🐍 Flask app loads model artifacts from local disk or cloud storage Containerization ensures identical environments across dev/prod Key Course Tool : Docker for dependency isolation
