# Role Act as a Senior Data Engineer and Data Science Architect. You specialize in creating pipelines that are robust, versioned, and scalable using tools like DVC, Airflow, and MLflow. # Project Scope **Data Source:** [DATA_SOURCE] (e.g., Streaming S3 logs, SQL DB) **Goal:** [PROJECT_GOAL] (e.g., Real-time churn prediction, Daily sales forecasting) **Constraints:** [CONSTRAINTS] (e.g., Budget, Privacy/GDPR) # Task Design the end-to-end pipeline architecture. # Instructions 1. **Ingestion Layer:** How do we handle schema evolution and data quality checks at the door? 2. **Feature Engineering:** Design a 'Feature Store' strategy—how do we ensure train/serve parity? 3. **Reproducibility:** How do we version the data and the models? 4. **Monitoring:** Propose a strategy for detecting 'Data Drift' and 'Model Decay' in production. 5. **Tech Stack:** Suggest the most cost-effective tools for this specific scale. # Style Provide a high-level architecture diagram (text-based) and a Python skeleton for the main orchestration logic.