Build Your Own AI
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Build Your Own AI is a comprehensive, practical guide for developers seeking to master the creation of real-world AI applications using Python. Authored by a Google Senior Staff Software Engineer and published by Manning, this book covers essential modern AI concepts like Large Language Models (LLMs), Retrieval Augmented Generation (RAG), and AI agents. It emphasizes hands-on code examples and project-based learning to bridge the gap between theoretical knowledge and practical application, enabling developers to build, deploy, and monitor robust AI systems.
What It Does
This tool, presented as a book, serves as an educational resource that teaches developers how to design, build, and deploy AI applications. It provides structured learning modules, detailed explanations of core AI concepts, and practical Python code examples. The guide walks users through creating functional AI projects from scratch, covering everything from foundational Python skills to advanced deployment strategies.
Pricing
Pricing Plans
Purchase the digital version of the book for convenient reading on any device.
- Digital access to the complete book
Receive a physical, printed copy of the book delivered to your address.
- Physical copy of the book
Get both the digital and physical versions of the book for maximum flexibility.
- Digital access
- Physical copy of the book
Core Value Propositions
Practical Skill Development
Acquire hands-on abilities to build and deploy modern AI applications, moving beyond theoretical understanding.
Real-World Project Experience
Gain experience by building functional AI projects, enhancing portfolios and practical problem-solving skills.
Master Modern AI Techniques
Learn to implement cutting-edge AI concepts like LLMs, RAG, and AI agents that are highly sought after in the industry.
Expert-Authored Content
Benefit from insights and best practices shared by a Senior Staff Software Engineer from Google, ensuring high-quality, relevant instruction.
Use Cases
Building Intelligent Chatbots
Develop conversational AI agents for customer service, internal tools, or interactive user experiences using LLMs and RAG.
Creating Semantic Search Engines
Implement advanced search functionalities that understand context and meaning, improving information retrieval from documents or databases.
Developing Autonomous AI Agents
Design and build AI agents capable of performing multi-step tasks, using tools, and making decisions independently.
Constructing Code Assistants
Create AI tools that assist with code generation, completion, debugging, and review, enhancing developer productivity.
Implementing RAG for Knowledge Bases
Build systems that augment LLMs with private or specialized data sources to generate highly accurate and relevant responses.
Deploying AI Applications to Production
Learn to package and deploy AI models and applications to cloud environments using serverless functions or APIs.
Technical Features & Integration
LLM & Prompt Engineering
Teaches how to effectively use and engineer prompts for Large Language Models to achieve desired outputs and behaviors.
Retrieval Augmented Generation (RAG)
Covers implementing RAG systems to enhance LLM knowledge with external, up-to-date data for more accurate responses.
AI Agents & Multi-Agent Systems
Explores building autonomous AI agents capable of using tools, memory, and coordinating with other agents for complex tasks.
Practical Python Code Examples
Provides extensive hands-on Python code examples that illustrate concepts and facilitate direct application in projects.
Project-Based Learning
Guides users through building complete AI applications like semantic search, chatbots, and code assistants for practical experience.
Deployment & Monitoring Strategies
Details methods for deploying AI applications using API wrappers and serverless architectures, along with monitoring best practices.
Foundational AI & ML Concepts
Establishes a strong base in machine learning fundamentals, embeddings, and vector databases relevant to modern AI.
Ethical AI Development
Addresses crucial ethical considerations and best practices for building responsible and fair AI systems.
Target Audience
This guide is ideal for software developers, data scientists, and AI/ML engineers who want to acquire practical skills in building and deploying modern AI applications. It caters to those with a foundational understanding of Python looking to specialize in LLMs, RAG, and AI agents. Students and hobbyists eager to transition theoretical AI knowledge into tangible projects will also find it highly beneficial.
Frequently Asked Questions
Build Your Own AI is a paid tool. Available plans include: eBook, Print Book, eBook + Print Book.
This tool, presented as a book, serves as an educational resource that teaches developers how to design, build, and deploy AI applications. It provides structured learning modules, detailed explanations of core AI concepts, and practical Python code examples. The guide walks users through creating functional AI projects from scratch, covering everything from foundational Python skills to advanced deployment strategies.
Key features of Build Your Own AI include: LLM & Prompt Engineering: Teaches how to effectively use and engineer prompts for Large Language Models to achieve desired outputs and behaviors.. Retrieval Augmented Generation (RAG): Covers implementing RAG systems to enhance LLM knowledge with external, up-to-date data for more accurate responses.. AI Agents & Multi-Agent Systems: Explores building autonomous AI agents capable of using tools, memory, and coordinating with other agents for complex tasks.. Practical Python Code Examples: Provides extensive hands-on Python code examples that illustrate concepts and facilitate direct application in projects.. Project-Based Learning: Guides users through building complete AI applications like semantic search, chatbots, and code assistants for practical experience.. Deployment & Monitoring Strategies: Details methods for deploying AI applications using API wrappers and serverless architectures, along with monitoring best practices.. Foundational AI & ML Concepts: Establishes a strong base in machine learning fundamentals, embeddings, and vector databases relevant to modern AI.. Ethical AI Development: Addresses crucial ethical considerations and best practices for building responsible and fair AI systems..
Build Your Own AI is best suited for This guide is ideal for software developers, data scientists, and AI/ML engineers who want to acquire practical skills in building and deploying modern AI applications. It caters to those with a foundational understanding of Python looking to specialize in LLMs, RAG, and AI agents. Students and hobbyists eager to transition theoretical AI knowledge into tangible projects will also find it highly beneficial..
Acquire hands-on abilities to build and deploy modern AI applications, moving beyond theoretical understanding.
Gain experience by building functional AI projects, enhancing portfolios and practical problem-solving skills.
Learn to implement cutting-edge AI concepts like LLMs, RAG, and AI agents that are highly sought after in the industry.
Benefit from insights and best practices shared by a Senior Staff Software Engineer from Google, ensuring high-quality, relevant instruction.
Develop conversational AI agents for customer service, internal tools, or interactive user experiences using LLMs and RAG.
Implement advanced search functionalities that understand context and meaning, improving information retrieval from documents or databases.
Design and build AI agents capable of performing multi-step tasks, using tools, and making decisions independently.
Create AI tools that assist with code generation, completion, debugging, and review, enhancing developer productivity.
Build systems that augment LLMs with private or specialized data sources to generate highly accurate and relevant responses.
Learn to package and deploy AI models and applications to cloud environments using serverless functions or APIs.
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