Gensim in Practice: Building Scalable NLP Systems with Topic Models, Embeddings, and Semantic Search is an authoritative, hands-on guide to using Gensim to build robust, scalable natural language processing systems. Beginning with the library's origins, architecture, and place in the Python scientific ecosystem, the book contrasts Gensim with other NLP frameworks and shows how to design memory- and compute-efficient pipelines that scale from research prototypes to production services.

The core of the book covers foundational and advanced vector-space techniques and embeddings—Bag-of-Words, TF-IDF, LSA, LDA, Word2Vec, FastText, and Doc2Vec—alongside practical guidance on preprocessing, corpus management, model evaluation, interpretability, and hyperparameter optimization. Each concept is grounded in reproducible examples and industrial best practices so practitioners gain both the theoretical background and the hands-on skills needed to deploy reliable, performant models.

Beyond core text processing, the book explores multimodal and domain-specific workflows, semantic search, and integration with diverse data sources and systems. Chapters on production hardening address observability, security, parallel computation, and ethical AI, while forward-looking guidance covers custom model extensions, knowledge graph integration, and using Gensim in concert with large language models—making this an essential resource for engineers and researchers building responsible, scalable NLP solutions.