Applied spaCy: Practical Techniques for Building Robust NLP Pipelines and Production Systems

Applied spaCy: Practical Techniques for Building Robust NLP Pipelines and Production Systems is a hands‑on guide that teaches engineers and researchers how to design, build, and maintain industrial-grade NLP solutions using spaCy. Grounded in real-world workflows, the book moves beyond tutorials to demonstrate practical decisions and patterns for reliable, scalable systems—covering environment and dependency management, cross-platform deployment, and how spaCy fits within the broader NLP ecosystem alongside tools like NLTK, CoreNLP, Stanza, and Transformers.

The core chapters unpack spaCy’s data structures and pipeline mechanics—tokenization, segmentation, morphological analysis, part‑of‑speech tagging, dependency parsing, and named entity recognition—while emphasizing extensibility and error analysis. Readers learn to craft custom components, mix rule‑based and statistical approaches, handle multilingual and large‑scale data, and profile and optimize pipelines for throughput and latency. Annotation best practices, tooling for quality control, and techniques for integrating spaCy with scikit‑learn and transformer models are presented as pragmatic, repeatable patterns.

Advanced sections cover the full model lifecycle: preparing data, training and fine‑tuning models, active learning, explainability, privacy considerations, and deploying models to cloud and on‑device environments. The book closes with production engineering guidance—monitoring, versioning, testing, and continuous improvement—alongside a call to contribute to the spaCy ecosystem and to adopt responsible, ethical practices when delivering NLP systems in the real world.