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The Complete AI Engineer Bootcamp Bundle
Build, test, deploy, and scale real-world AI applications with this two-volume engineering bootcamp. Learn Python, FastAPI, LLMs, semantic search, RAG, Docker, CI/CD, Kubernetes, cloud deployment, security, and production monitoring. One complete learning path from AI foundations to production-ready systems.
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About
About the Bundle
The Complete AI Engineer Bootcamp Bundle
From Building AI Applications to Deploying and Scaling Them in Production
Move beyond AI prototypes and develop the engineering skills needed to build, deploy, secure, monitor, and scale real-world AI applications.
This bundle brings together both volumes of The AI Engineer into one structured, project-based learning path.
Volume I: Build the Foundations
Explore Python engineering, production APIs, databases, authentication, testing, AI fundamentals, embeddings, semantic search, retrieval-augmented generation (RAG), LLM application architecture, caching, background jobs, and performance engineering.
Volume II: Deploy. Scale. Operate.
Continue into Docker, Linux operations, Git and GitHub workflows, CI/CD, cloud deployment, Kubernetes, observability, production security, scaling strategies, and production readiness.
What You Will Learn
- Design and structure maintainable Python applications.
- Build APIs and backend services for AI-powered products.
- Understand embeddings, semantic search, RAG, and LLM application architecture.
- Test AI systems and investigate failures systematically.
- Containerize applications and automate deployment workflows.
- Monitor production systems and reason about reliability, security, and performance.
- Connect application architecture, infrastructure, and operational practices into a coherent engineering system.
Who Is This Bundle For?
Python developers, backend engineers, aspiring AI engineers, and software developers who want to progress from building individual AI features to engineering complete, production-oriented AI systems.
Two volumes. One engineering journey. Build the system, deploy it, and learn how to operate it with confidence.
Books
About the Books
The AI Engineer Bootcamp in a Book
Build the Foundations
The AI Engineer — Volume I: Build the Foundations is the first part of a project-based engineering journey for developers who want to move from traditional Python development into modern AI engineering.
AI engineering is much more than calling an LLM API.
A production AI system requires software architecture, APIs, databases, authentication, testing, reliability engineering, embeddings, semantic search, retrieval-augmented generation, LLM architecture, asynchronous processing, caching, and performance optimization.
This volume builds those foundations step by step.
You will begin by developing the mindset of a production engineer and learn how to structure professional Python projects. From there, you will build production APIs with FastAPI, design persistent data systems with PostgreSQL, implement authentication and authorization, and establish testing and reliability practices.
The book then moves into AI engineering foundations.
You will explore vectors and embeddings, semantic similarity, semantic search, retrieval-augmented generation, tokenization, attention, LLM architecture, context management, model providers, structured outputs, and AI system reliability.
The final part of the volume brings these concepts together into an AI-powered knowledge platform capable of processing technical documents, creating searchable representations, retrieving relevant information, and supporting grounded AI interactions.
What You Will Learn
- How to think like a production AI Engineer
- How to structure professional Python applications
- How to build production APIs with FastAPI
- How to design PostgreSQL database systems
- How authentication and authorization work in production
- How to test AI-enabled applications
- How to debug and engineer reliable systems
- How embeddings represent semantic information
- How semantic search works
- How vector similarity is calculated
- How Retrieval-Augmented Generation works
- How modern LLM applications are architected
- How to design provider-independent AI systems
- How to build AI processing pipelines
- How to handle document ingestion and processing
- How to use caching and background processing
- How to optimize AI application performance
Who This Book Is For
This book is designed for:
- Python developers moving into AI engineering
- Backend developers building AI-powered applications
- Software engineers working with LLMs and GenAI
- Developers interested in semantic search and RAG
- Engineers who want to move beyond AI prototypes
- Students building serious AI engineering projects
- Technical professionals who want a production-oriented understanding of AI systems
This is not a book about using AI as a black box.
It is about understanding the engineering systems built around AI.
By the end of Volume I, you will have the foundations required to continue toward production deployment, cloud infrastructure, Kubernetes, observability, security, scaling, and complete AI system architecture in Volume II.
Learn the foundations. Build the system. Think like an AI Engineer.
The AI Engineer — Volume II: Deploy. Scale. Operate.
A Practical Guide to Docker, CI/CD, Cloud Deployment, Kubernetes, Observability, Security, and Scaling Production AI Systems
Your AI Application Works. But Is It Ready for Production?
Building an AI application is only the beginning. Running it reliably in production requires a different level of engineering: deployment automation, containerization, security, monitoring, performance optimization, and the ability to recover when things fail.
The AI Engineer — Volume II: Deploy. Scale. Operate. takes you beyond development and into the engineering practices required to operate real-world AI systems.
Through practical examples and a project-based approach, you will explore:
- Docker and containerization — package applications and their dependencies into reproducible environments.
- Linux and production operations — understand the environment where your applications run.
- Git, GitHub, and CI/CD — build reliable engineering workflows and automate testing and deployment.
- Cloud deployment and Kubernetes — deploy applications and understand orchestration, scaling, and recovery.
- Observability and monitoring — use logs, metrics, and operational signals to investigate failures and understand system behavior.
- Production security — protect APIs, credentials, data, and AI-powered workflows.
- Performance and scalability — reason about bottlenecks, caching, background jobs, database pressure, and LLM latency.
- Architecture and production readiness — connect the individual engineering practices into a coherent, maintainable system.
The volume progresses from deployment fundamentals to cloud-native operations, security, scaling, architecture patterns, and a final engineering challenge designed to bring the concepts together.
A Note on Code and Architecture: This book is a comprehensive systems engineering guide, not a simple copy-paste code repository. While complete code snippets, configurations, and hands-on examples are provided throughout, the primary focus is on understanding architectural trade-offs, system boundaries, and production reliability. You will learn how and why production systems are built, not just run raw scripts.
This book is for Python developers, backend engineers, AI engineers, and software developers who want to move beyond prototypes and build systems they can deploy, monitor, secure, and operate with confidence.
You do not need to master every infrastructure tool before you begin. The goal is to develop the engineering judgment to understand how the pieces fit together, why production systems fail, and how to design better solutions.
Build systems that do more than work on your machine. Build systems that are ready for the real world.
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