Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Applied spaCy: Practical Techniques for Building Robust NLP Pipelines and Production Systems

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

E. Clark, William

Paperback

General Computers

ISBN13: 9798267282147
Publisher: Independently Published
Published: Oct 12 2025
Pages: 312
Weight: 0.92
Height: 0.65 Width: 6.00 Depth: 9.00
Language: English
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.

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