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Home > Computing and Information Technology > Computer science > Artificial intelligence > AI Engineering: Building Multi-Modal Intelligent Systems with Vision, Language, and Audio From LLM Fine-Tuning to Voice Agents, AR Interfaces, and Real-World Deploy
AI Engineering: Building Multi-Modal Intelligent Systems with Vision, Language, and Audio From LLM Fine-Tuning to Voice Agents, AR Interfaces, and Real-World Deploy

AI Engineering: Building Multi-Modal Intelligent Systems with Vision, Language, and Audio From LLM Fine-Tuning to Voice Agents, AR Interfaces, and Real-World Deploy


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About the Book

AI Engineering: Building Multi-Modal Intelligent Systems with Vision, Language, and Audio From LLM Fine-Tuning to Voice Agents, AR Interfaces, and Real-World Deployment Unlock the future of artificial intelligence with practical, production-ready multi-modal engineering. This hands-on guide is built for developers, researchers, and AI professionals who want to go beyond chatbots and dive into building intelligent systems that understand text, images, audio, and human intent - all in one pipeline. Whether you're fine-tuning large language models (LLMs) or creating voice-driven AR interfaces, this book walks you through the real engineering decisions, tools, and architectures needed to bring multi-modal AI to life. What You'll Learn: Fine-tuning Large Language Models (LLMs): Train and adapt models like GPT-2, LLaMA, and Mistral for custom tasks using Hugging Face, LoRA, QLoRA, and PEFT. Voice Interfaces: Combine Whisper, LLMs, and Bark/Tortoise TTS to build interactive speech-driven assistants. Computer Vision + Language: Use models like BLIP, CLIP, and DETR to connect what systems see to what they say and understand. Instruction Tuning & Hyperparameter Optimization: Build smarter, domain-specific models with efficient training workflows. Multi-Modal Pipelines: Chain audio, image, and text inputs for question answering, summarization, tutoring, and AR/robotic control. Real-Time Interfaces: Deploy intelligent agents using FastAPI, Streamlit, Gradio, Docker, and Hugging Face Spaces. Edge & Offline Deployment: Optimize models with ONNX, quantization (4-bit, 8-bit), and TensorRT for low-latency inference on CPU/GPU. Use Cases Covered: Smart document summarizers with OCR + TTS Voice-enabled image assistants Emotion-aware agents Virtual tutors AR-enhanced AI interfaces Robotic perception + control from voice/image input Secure, multilingual, and privacy-conscious AI systems Tools & Frameworks Inside: Python, PyTorch, Hugging Face Transformers LangChain, OpenCV, Whisper, TTS, BLIP ROS, Unity (AR/VR), Gradio, Streamlit Docker, FastAPI, gRPC, TorchServe Built for engineers. Written with depth. Designed for real-world impact. If you're ready to build intelligent multi-modal agents that understand the world like humans do - across speech, vision, and language - this book gives you the complete roadmap. Perfect for: Machine learning engineers, data scientists, AI product developers, researchers, robotics engineers, and anyone building cutting-edge AI systems.


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Product Details
  • ISBN-13: 9798296089038
  • Publisher: Independently Published
  • Publisher Imprint: Independently Published
  • Height: 229 mm
  • No of Pages: 296
  • Returnable: N
  • Sub Title: Building Multi-Modal Intelligent Systems with Vision, Language, and Audio From LLM Fine-Tuning to Voice Agents, AR Interfaces, and Real-World Deploy
  • Width: 152 mm
  • ISBN-10: 8296089033
  • Publisher Date: 01 Aug 2025
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Spine Width: 16 mm
  • Weight: 449 gr


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