Unlocking the Future of Data Pipelines: The Power of Evaluation in AI(E)TL
For over 20 years, I’ve mastered the data pipeline process: Extract → Transform → Load. But with the integration of AI, I discovered a game-changing addition—Evaluation. This pivotal step redefines how we handle data quality.
Why Add Evaluation?
- Quality validation: Ensures AI-generated outputs are reliable.
- Semantic consistency: Guarantees that outputs maintain meaning, even when not identical.
- Cost-effective: Mitigates unnecessary expenses by preventing errors before they propagate.
Key Changes in AI(E)TL:
- Immutability incorporates context.
- Idempotency shifts to semantic similarity.
- Testing focuses on distributional properties.
- Monitoring tracks quality metrics.
Maintain the core data engineering principles while adapting to the AI landscape. The paradigm shift is here—it’s time to embrace it.
🔗 How are you integrating AI in your pipelines? Share your thoughts! Want to learn more? Check out “The LLM Evaluation Stack” for deeper insights.
