Scaling Data Labeling Operations

Architecture patterns and best practices for scaling TigerLabel to support 100+ concurrent annotators and millions of training samples.

Horizontal Scaling Strategies

• Kubernetes horizontal pod autoscaling based on CPU/memory
• Database read replicas for annotation queries
• CDN for static assets and training images
• Queue-based processing for export and AI pre-labeling jobs

Performance Optimization for Large Datasets

1. Enable image tiling for high-resolution images (satellite, medical)
2. Use lazy loading and pagination for annotation lists
3. Configure Redis clustering for session caching
4. Set up comprehensive monitoring and alerting
5. Implement project-based data partitioning