NLP Production Pipeline — High-Volume Text Processing System

I will build a production-grade NLP text processing infrastructure — covering a message queue for high-volume document ingestion, parallel NLP processing workers with your specified pipeline (classification, extraction, summarisation, or other), result storage and indexing, processing monitoring, and auto-scaling configuration for variable load. NLP pipelines handling thousands of documents per hour need infrastructure engineering as much as NLP modelling — a queue-based architecture with worker autoscaling, processing status tracking, and failure retry logic is what separates a production NLP system from a Python script that processes files in a for-loop.

The infrastructure covers a message queue (RabbitMQ, Kafka, or SQS) for document ingestion, worker pool with your NLP pipeline, result database and search index, processing status tracking API, autoscaling configuration, monitoring and alerting, and Docker/Kubernetes deployment.