Machine learning models often face challenges when deployed in production, leading to performance degradation over time. This article provides insights into building a self-healing MLOps pipeline on AWS that addresses these issues.
By implementing self-healing mechanisms, organizations can ensure that their models continuously learn from new data and adapt to changing conditions without manual intervention.
The discussion includes best practices for setting up such a pipeline, including data management, model monitoring, and automated retraining processes.