NLP Pipeline Architect
Design and optimize production-grade NLP pipelines covering text preprocessing, tokenization, NER, sentiment analysis, and transformer-based architectures using spaCy and HuggingFace.
SupaScore
84.4Best for
- ▸Design multilingual sentiment analysis pipeline for customer feedback processing
- ▸Build production NER system for extracting entities from legal documents
- ▸Optimize BERT-based text classification pipeline for latency-critical applications
- ▸Architect transformer-based document processing workflow with spaCy integration
- ▸Design domain adaptation strategy for medical text processing using HuggingFace models
What you'll get
- ●Step-by-step pipeline architecture with specific spaCy components, tokenization strategy, and HuggingFace model selection with performance benchmarks
- ●Production deployment blueprint including model distillation recommendations, batching strategies, and caching layers with latency estimates
- ●Comprehensive training strategy with data requirements, evaluation metrics, and domain adaptation approach using transfer learning principles
Not designed for ↓
- ×Training large language models from scratch or LLM fine-tuning strategies
- ×Computer vision or multimodal AI pipeline design
- ×Real-time speech processing or audio transcription pipelines
- ×Generative text applications like chatbots or content creation
Clear specification of NLP task requirements including input text characteristics, target languages, output format, and production constraints like latency and throughput.
Detailed pipeline architecture with specific preprocessing steps, model recommendations, training strategies, and production deployment considerations with performance trade-offs.
Evidence Policy
Enabled: this skill cites sources and distinguishes evidence from opinion.
Research Foundation: 8 sources (2 official docs, 3 paper, 2 books, 1 industry frameworks)
This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.
Version History
Initial release
Prerequisites
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Works well with
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Common Workflows
Production NLP System Development
End-to-end workflow from NLP pipeline design through production deployment and monitoring
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