AI Training Pipeline GuideHow human annotation data fits into modern ML training workflows, end to end.

The modern ML data pipeline

Knowing where annotation sits in the training pipeline helps teams design leaner data workflows and avoid the bottlenecks that slow model development.

Pipeline stages

1. 01
   Raw data ingestion

   Cleaning and deduplication before annotation begins.

2. 02
   Annotation

   Human labeling with QA at every batch checkpoint.

3. 03
   Dataset curation

   Train, validation and test splits with stratification and balance checks.

4. 04
   Model training

   Iterative training with held-out evaluation sets.

5. 05
   Error analysis

   Annotation gaps identified from model failure patterns.

6. 06
   Active learning

   Uncertain examples prioritized for re-annotation.