Jan 2026
Reliable NLP Pipelines in Production
1 min read • AI/ML
Building production NLP pipelines requires more than just model accuracy. During my internship at Musikaar, I learned that reliability comes from stable preprocessing and clear error handling.
## The Challenge
When processing 10,000+ HRMS records, inconsistent tokenization and preprocessing led to unpredictable model behavior. Small variations in input formatting caused significant accuracy drops.
## Key Learnings
1. **Normalize Early**: Standardize text inputs before tokenization
2. **Validate Outputs**: Check tokenized sequences match expected formats
3. **Handle Edge Cases**: Empty strings, special characters, and encoding issues
4. **Monitor Performance**: Track preprocessing time and memory usage
## Implementation
I built a pipeline with:
- Consistent tokenization using spaCy
- Input validation at each stage
- Error logging for debugging
- Performance metrics tracking
The result? 20% improvement in model stability and 40% reduction in manual processing effort.