Ml Engineer

This is a solid MLOps focused skill that covers the full production ML lifecycle, from building pipelines through deployment and monitoring. You'd reach for it when you need to move models from notebooks into production systems, whether that's real-time APIs, batch jobs, or edge devices. It handles the engineering side of ML: experiment tracking, CI/CD for models, feature stores, drift detection, and distributed training infrastructure. With 146 installs and passing security audits from Socket and Snyk, it's seeing real use. The scope is comprehensive enough to be your go-to for production ML work without trying to replace your actual data science workflow.