What are the data integration patterns for training and fine-tuning AI models?

Data integration for AI training requires collecting, cleaning, and transforming data from multiple sources into training-ready datasets. Pipeline architecture: extract data from source systems (databases, APIs, file storage, event streams), transform and clean (deduplication, normalization, PII removal, quality filtering), and load into training infrastructure (cloud storage, dataset registries). ETL for AI training: implement data versioning (DVC, LakeFS) to reproduce any training dataset, maintain data lineage tracking (where each training sample originated), and apply consistent preprocessing across training and inference. Data quality: implement automated quality checks (completeness, consistency, freshness, accuracy), flag and quarantine low-quality samples, track data distribution over time to detect drift, and maintain held-out test sets that are never used for training. Integration patterns: batch integration (daily/weekly data exports for periodic retraining), streaming integration (continuous data flow for online learning), and federated integration (training on distributed data without centralization). Annotation pipeline: integrate labeling tools (Label Studio, Labelbox) into the data pipeline, implement active learning to prioritize the most informative samples for labeling, and track inter-annotator agreement. Compliance: maintain consent records for training data, implement data deletion workflows for GDPR/CCPA requests that propagate to model retraining, and document data sources for AI transparency requirements.