
eBook - ePub
Mastering AI System Design
Architect, Build and Deploy AI Systems Using 10 Domain Driven Blueprints and Interview Strategies (English Edition)
- English
- ePUB (mobile friendly)
- Available on iOS & Android
eBook - ePub
Mastering AI System Design
Architect, Build and Deploy AI Systems Using 10 Domain Driven Blueprints and Interview Strategies (English Edition)
About this book
From Whiteboards to Workloads - Bridging AI Theory and Practice.Key Features? Practical frameworks, trade-off discussions, and mock interviews to prepare for modern system design.? Master LLMs, RAG, fine-tuning, edge AI, and multimodal systems through practical, domain-specific examples.? Connects academic AI foundations with industrial implementations to help readers design end-to-end systems.Book DescriptionSystem design is now a critical skill for AI professionals, enabling them to integrate data pipelines, model serving, orchestration, and monitoring into cohesive production ecosystems. Mastering AI System Design will guide you through that complete journeyāfrom understanding design principles and data workflows to building deployable AI architectures. It introduces core components of AI system design such as data engineering, model selection, evaluation metrics, API integration, and lifecycle management.Each chapter blends theory, architecture diagrams, and code-driven blueprints that cover real-world use casesāLLMs and prompt engineering, Retrieval-Augmented Generation (RAG), fine-tuning, supervised and unsupervised learning systems, recommendation engines, edge AI deployment, and multimodal transformers.By the end, you will be well-equipped to analyze trade-offs, design scalable inference pipelines, ensure model reliability, and apply system design frameworks for interviews and enterprise AI applications with confidence.What you will learn? Build end-to-end AI systems using proven frameworks for both interviews and real-world projects.? Design and implement LLM architectures, RAG pipelines, and fine-tuned models with hands-on guidance.? Develop supervised, unsupervised, recommendations, and multimodal AI systems across industries.? Architect domain-specific LLMs, sequence-to-sequence models, and edge-optimized vision systems.? Optimize, evaluate, and monitor AI systems for scalability, reliability, and performance.? Leverage modern AI tools and libraries including LangChain, Hugging Face, PyTorch, and TensorFlow.Table of Contents1. Introduction to AI System Design2. Crafting Intelligent Systems Using Prompt Engineering3. Developing Retrieval-Augmented Generation Systems4. Enhancing Systems Through LLM Finetuning5. Designing Financial Risk Prediction Systems Using Supervised Learning6. Implementing Unsupervised Learning Systems7. Building Recommendation Systems for E-Commerce8. Building Image Classification Models for Edge Devices9. Designing Sequence-to-Sequence Systems10. Building Domain-Specific LLMs from Scratch11. Building Multimodal Applications for HealthcareĀ Ā Ā Ā IndexAbout the AuthorsSoudamini Sreepada is a distinguished leader in Artificial Intelligence, education, and research, with over 18 years of experience in the technology industry. She teaches AI system design through DesignYourAI - https://www.designyourai. in. Her projects and resources are available on GitHub at https://github.com/ soudaminigit/After earning her M.Tech. in
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Yes, you can access Mastering AI System Design by Soudamini Sreepada in PDF and/or ePUB format, as well as other popular books in Computer Science & Artificial Intelligence (AI) & Semantics. We have over one million books available in our catalogue for you to explore.
Information
Table of contents
- Cover Page
- Title Page
- Copyright Page
- Dedication Page
- About the Author
- About the Technical Reviewer
- Acknowledgements
- Preface
- Get a Free eBook
- Errata
- Table of Contents
- 1. Introduction to AI System Design
- 2. Crafting Intelligent Systems Using Prompt Engineering
- 3. Developing Retrieval-Augmented Generation Systems
- 4. Enhancing Systems Through LLM Finetuning
- 5. Designing Financial Risk Prediction Systems Using Supervised Learning
- 6. Implementing Unsupervised Learning Systems
- 7. Building Recommendation Systems for E-Commerce
- 8. Building Image Classification Models for Edge Devices
- 9. Designing Sequence-to-Sequence Systems
- 10. Building Domain-Specific LLMs from Scratch
- 11. Building Multimodal Applications for Healthcare
- Index