mlflow-data-pipelines

MLflow for data engineering — tracking server setup (PostgreSQL backend + S3 artifacts), experiment tracking (params/metrics/artifacts/autolog), ETL job metadata logging (row counts, DQ metrics, lineage tags), Model Registry (register/alias/promote), model serving (pyfunc/REST API/batch scoring), MLproject files, Airflow integration (MLflowClientHook/XCom/model promotion), Spark integration…