Bringing MLflow and Data Pipelines Closer Together

This article discusses bridging the gap between data engineering and ML engineering by integrating MLflow 3 with data pipelines. It covers MLflow 3's new features like GenAI tracing, agent evaluation, and data quality monitoring, alongside Databricks' Data Quality Monitoring for drift detection and statistical distribution tracking. Topics include training data lineage, distinguishing model drift from data drift, CI/CD for ML pipelines, scheduling with Airflow, model registry deployment, and experiment tracking at scale. The goal is unified observability across data pipelines and ML models for better cross-boundary diagnosis.