How do we build reliable training data pipelines?
Good data pipelines automate everything.
How It Works:
Automate ingestion, cleaning, labeling, and versioning with tools like DVC or MLflow; integrate validation checks and monitoring for drift.
Key Benefits:
- Consistency across experiments
- Traceable lineage for compliance
- Rapid iteration with fresh data
Real-World Use Cases:
- Real-time clickstream ingestion for recommendation models
- Continuous labeling of incoming support tickets
FAQs
How handle schema changes?
How manage labels over time?
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