Integrating Automation in Data Science Workflows

This chapter explores the integration of automated machine learning (AutoML) into existing data science practices, focusing on feature optimization, model selection, and the importance of model fairness. It discusses the complexities of model deployment and the evolving role of MLOps, emphasizing the need for ongoing performance monitoring and automation in retraining processes. The chapter highlights recent advancements in AutoML and neural architecture and reflects on the evolving nature of automation in the field of data science.