Use Apache Airflow workflows to orchestrate data processing on Amazon SageMaker Unified Studio

This article demonstrates how to use Apache Airflow workflows in Amazon SageMaker Unified Studio to orchestrate a complete machine learning pipeline for predicting taxi fares.

• The pipeline involves three key tasks:
  • Ingesting and transforming weather data
  • Ingesting, transforming, and joining taxi data
  • Training and predicting using machine learning
• Workflow is created using Python-based Apache Airflow DAGs (Directed Acyclic Graphs)
• Uses NotebookOperator to execute notebooks sequentially
• Allows customization of workflow schedule and notebook paths
• Provides a centralized environment for data preparation, model training, and workflow orchestration

The solution demonstrates how SageMaker Unified Studio can help data practitioners collaborate effectively and operationalize AI/ML assets in a single, governed environment.