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.
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