Incorporating an unsupervised learning model within Databricks notebooks that continuously learns from incoming data, flagging anomalies before they reach downstream systems

Using Databricks MLflow for model management, automating the deployment of data quality models into production pipelines, and monitoring model performance

Leveraging Azure Machine Learning to periodically retrain data quality models, deploying these models as web services called by Databricks jobs for real-time quality checking

Training a machine learning model on historical data quality issues and integrating it into the CI/CD pipeline to evaluate new data batches pre-deployment