AI Automation for Data Science Workflows Guide
Implement AI in data science workflows by mapping the pipeline end-to-end, automating the highest-friction steps (data collection, data cleaning, feature engineering), using AutoML for model selection, and MLOps for deployment + drift monitoring. Agentic AI and AI agents can cut manual intervention, improve data quality, and shorten time to insights—if you add guardrails, metrics, and accountability.