mlflow-data-pipelines

Track ETL execution metadata with MLflow by configuring MLFLOW_TRACKING_URI and using MLflow APIs to log pipeline run parameters, metrics, tags, and artifacts. This records input/output row counts, processing times, and data quality metrics for pipeline run auditing.

Yes, MLflow tracking integrates with Airflow and Spark to record pipeline run metadata and model training outputs. It supports autologging for PySpark ML and scikit-learn, automatically capturing execution details within your Airflow DAGs and Spark jobs.

Register and promote models in the MLflow Model Registry by logging model training outputs, registering them, and promoting versions via aliases. This centralizes model lifecycle management for downstream REST API serving or batch scoring with pyfunc.

MLflow autologging works with XGBoost, scikit-learn, and PySpark ML flavors to automatically capture model training outputs, parameters, and metrics. This removes manual logging overhead and ensures consistent experiment tracking across nested runs.

Set up a production MLflow tracking server by configuring a PostgreSQL backend for metadata and S3 artifact storage. This architecture supports concurrency-oriented options like PgBouncer to handle high-volume ETL observability and model lifecycle management workloads.

Use MLflow Projects (MLproject files) for data engineering pipeline observability to standardize pipeline run environments and execution parameters. This ensures consistent tracking of lineage tags, data quality metrics, and deployable model artifacts across pipeline stages.