What role do automated pipelines play in MLOps?

Automated pipelines in MLOps streamline the end-to-end machine learning lifecycle by automating the processes of data ingestion, preprocessing, model training, evaluation, and deployment. This allows for consistent and repeatable workflows, reducing human error and increasing efficiency. Automated pipelines facilitate continuous integration and continuous delivery (CI/CD) practices, enabling teams to quickly and reliably incorporate changes, retrain models, and deploy updates to production environments without extensive manual intervention.