Example Concept: One major challenge is managing the data dependencies and ensuring data quality, as machine learning models are highly sensitive to input data changes. Another challenge is the integration of model versioning and deployment processes into existing CI/CD pipelines, which requires careful orchestration to maintain consistency and reliability. Additionally, testing and validating model outputs can be complex due to the probabilistic nature of predictions, necessitating specialized testing frameworks and metrics to evaluate model performance alongside traditional software testing.