{"id":14839,"date":"2026-08-10T11:19:41","date_gmt":"2026-08-10T11:19:41","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/oreilly-domain-specific-small-language-models-video-edition-2026-5\/"},"modified":"2026-08-10T11:19:41","modified_gmt":"2026-08-10T11:19:41","slug":"oreilly-domain-specific-small-language-models-video-edition-2026-5","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/oreilly-domain-specific-small-language-models-video-edition-2026-5\/","title":{"rendered":"Oreilly \u2013 Domain-Specific Small Language Models, Video Edition 2026-5"},"content":{"rendered":"
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Description<\/h2>\n

Domain-Specific Small Language Models, Video Edition is a course on designing, training, and deploying compact language models tailored to specific domains and industry applications, published by Oreilly Online Academy. This course is a practical guide to designing, training, and deploying compact language models tailored to specific domains and industry applications. Designed for AI engineers, machine learning specialists, data scientists, and developers, the course focuses on creating efficient small language models (SLMs) that deliver high performance while requiring fewer computational resources than large, general-purpose models. In the video editions, a narrator reads the book while the content, figures, code listings, charts, and text are displayed on the screen. Like an audiobook, you can also watch it as a video.<\/p>\n

Students will learn domain-specific data preparation, model selection, fine-tuning techniques, efficient parameter training methods, evaluation strategies, retrieval-augmented generation (RAG), model optimization, and deployment for edge devices and enterprise environments. When you need a language model to accurately and quickly answer questions about a specific domain of knowledge, the extensive capacity of an LLM can be more of a hindrance than a help. The Domain-Specific Small Language Models course teaches you to build generative AI models optimized for specific domains.<\/p>\n

What you will learn in Domain-Specific Small Language Models, Video Edition:<\/h3>\n