Best practices for AI/ML data pipelines include:

• Ensure data quality: Validate and enrich data early using tools like Snowplow Enrich.
• Design for scalability: Build pipelines that can handle increasing data volumes and complexity.
• Maintain feedback loops: Monitor model outputs and performance to inform future iterations.
• Modularize and automate: Use orchestration tools (e.g., Airflow, Dagster) and modular components (e.g., dbt, feature stores) to streamline processes.
• Monitor data and models: Continuously track input data and model performance metrics to detect issues quickly.
  
  

Snowplow plays a crucial role in collecting accurate, real-time behavioral data at scale, making it a strong foundation for ML data pipelines.