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AI for Video: A Complete Guide to Intelligence in Video Products: From Models and Inference to Computer Vision, Speech, Search, and Generative Video
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Publisher: Independently Published
Pages: 708
Weight: 2.66
Height: 1.42 Width: 7.00 Depth: 10.00
Language: English
Every video product now has an AI feature on the roadmap, and the literature is no help: research papers optimise benchmarks nobody ships, vendor blogs sell an API, and neither tells you what the feature costs per stream-hour or how it fails at three in the morning. This volume is the first half of the book that does - a map of where intelligence lives in a video product, the machine-learning minimum needed to make decisions about it, and then the capabilities themselves: seeing, understanding, enhancing and generating.
It opens with the product map, the lifecycle of an AI feature, deployment topologies, model artifacts and runtimes, and the economics - the chapter that decides most of the rest. Then the ML minimum: how machines learn, CNNs, transformers and embeddings, adaptation and evaluation. Then computer vision on video - preprocessing, object detection, open-vocabulary models and the VLM boundary, segmentation, tracking and optical flow, faces and poses with the law attached, OCR and depth, anomaly detection. Then understanding: CLIP-style embeddings, video VLMs, ASR and diarization, domain fine-tuning, and multimodal RAG over a video archive. It closes on enhancement and generation - restoration and upscaling, the principles of generative video and how to integrate it, voice generation and talking heads, and the quality gates and provenance that keep it shippable.
Every topic reads on its top layer without mathematics and is still accurate; the rigorous layer sits underneath, in boxes you can skip. Each chapter ends with review questions, problems, and a lab you run on Python, PyTorch and open models.
What is inside- The map - AI in a video product, the feature lifecycle, deployment topologies, model artifacts and runtimes, the economics
- The ML minimum - how machines learn, CNNs, transformers, embeddings, adaptation and evaluation
- Computer vision on video - preprocessing, detection, open-vocabulary and VLMs, segmentation, tracking, faces and poses, OCR and depth, anomaly detection
- Understanding - embeddings and CLIP, video VLMs, speech understanding, domain fine-tuning, multimodal RAG over an archive
- Enhancement and generation - video enhancement, generative video principles and integration, voice and talking heads, quality gates and provenance
Video and backend engineers taking on an AI feature, ML engineers moving into video, and technical leads who must defend a build-or-buy decision with a cost model. Python and a command line are enough; the ML prerequisites are in an appendix.
26 chapters, 7 appendices, 190 figures, a 476-entry bibliography, an 800-entry glossary and index, and 27 labs. Book 6 of 7, Volume 1, in The Fora Soft Video Engineering Handbook; Volume 2 covers real time, agents, operations and verticals.
Nikolay Sapunov is CEO and co-founder of Fora Soft, which has built video, real-time and AI systems since 2005: 250+ delivered projects, a 50-engineer team, systems used by NBC, Coca-Cola and Red Bull. This series is the training the company uses on its own engineers.
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