AutoML Pipeline Orchestration and Explainable AI Integration in Databricks Environments

This study explores the integration of automated machine learning (AutoML) capabilities with explainable AI frameworks within Databricks ecosystems for enterprise-scale deployment. The research presents a comprehensive methodology for automated model selection, hyperparameter optimization, and interpretability analysis that addresses regulatory compliance requirements while maintaining production-grade performance. Novel contributions include adaptive algorithm selection based on data characteristics, automated bias detection mechanisms, and real-time explainability dashboards for production models. The proposed framework demonstrates a 65% reduction in model development time while ensuring regulatory compliance through integrated fairness metrics and interpretability standards. Performance evaluation across multiple industry datasets shows consistent accuracy improvements of 12-18% compared to traditional manual ML approaches, with automated bias detection achieving 94% accuracy in identifying potential fairness violations before model deployment.