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 - Softcover
Synopsis
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.
"synopsis" may belong to another edition of this title.
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Paperback. Condition: new. Paperback. AI Engineering: Building Multi-Modal Intelligent Systems with Vision, Language, and AudioFrom LLM Fine-Tuning to Voice Agents, AR Interfaces, and Real-World DeploymentUnlock 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 + TTSVoice-enabled image assistantsEmotion-aware agentsVirtual tutorsAR-enhanced AI interfacesRobotic perception + control from voice/image inputSecure, multilingual, and privacy-conscious AI systemsTools & Frameworks Inside: Python, PyTorch, Hugging Face TransformersLangChain, OpenCV, Whisper, TTS, BLIPROS, Unity (AR/VR), Gradio, StreamlitDocker, FastAPI, gRPC, TorchServeBuilt 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9798296089038
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Paperback. Condition: new. Paperback. AI Engineering: Building Multi-Modal Intelligent Systems with Vision, Language, and AudioFrom LLM Fine-Tuning to Voice Agents, AR Interfaces, and Real-World DeploymentUnlock 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 + TTSVoice-enabled image assistantsEmotion-aware agentsVirtual tutorsAR-enhanced AI interfacesRobotic perception + control from voice/image inputSecure, multilingual, and privacy-conscious AI systemsTools & Frameworks Inside: Python, PyTorch, Hugging Face TransformersLangChain, OpenCV, Whisper, TTS, BLIPROS, Unity (AR/VR), Gradio, StreamlitDocker, FastAPI, gRPC, TorchServeBuilt 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798296089038
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