Resource Guide
AI Training Pipeline Guide
End-to-end overview of how human annotation data integrates into modern ML training workflows.
The Modern ML Data Pipeline
Understanding where annotation fits in the ML training pipeline helps teams design more efficient data workflows and avoid common bottlenecks that slow model development.
Pipeline Stages
01
Raw Data Ingestion
Cleaning and deduplication before annotation begins
02
Annotation
Human labeling with QA at every batch checkpoint
03
Dataset Curation
Train/val/test splits with stratification and balance checks
04
Model Training
Iterative training with held-out evaluation sets
05
Error Analysis
Identify annotation gaps from model failure patterns
06
Active Learning
Prioritize uncertain examples for re-annotation
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