How do you implement natural language processing pipelines for production applications?
Production NLP pipelines process text at scale with reliability and monitoring. Pipeline architecture: text preprocessing (encoding normalization, cleaning, language detection), core NLP (tokenization, POS tagging, NER, dependency parsing), task-specific models (classification, extraction, summarization, translation), post-processing (output formatting, validation, confidence scoring), and serving (API endpoint with caching and monitoring). Implementation: use spaCy for fast, production-ready NLP processing (tokenization, NER, POS tagging at 10K+ documents/second), Hugging Face Transformers for state-of-the-art task-specific models, and custom models fine-tuned on domain data. Text preprocessing: handle encoding issues (UTF-8 normalization, mojibake repair), implement text cleaning appropriate for the domain (HTML stripping for web content, header/footer removal for emails, metadata extraction for documents), and detect language for routing to language-specific models. Entity extraction: combine model-based NER (pre-trained or fine-tuned) with rule-based patterns (regex for structured entities like dates, phone numbers, IDs), implement entity linking (map extracted entities to canonical entries in a knowledge base), and handle entity coreference (resolve "it", "the company", "they" to specific entities). Scaling: implement batch processing with configurable parallelism, use GPU inference for transformer models (batch size optimization for throughput), implement model caching (keep loaded models in memory between requests), and implement result caching for repeated inputs. Monitoring: track processing latency per pipeline stage, measure extraction accuracy using a labeled evaluation set, monitor input text characteristics (length distribution, language distribution, encoding issues), and alert on quality degradation. Error handling: implement graceful degradation (if NER fails, return partial results with confidence indicators), maintain error logs with input context for debugging, and implement retry logic for transient failures.