Key Features
Advanced Python Coding for AI is a practical guide for developers who want to use Python well in real AI systems. It shows how to write Python that can support retrieval, model workflows, evaluation, automation, APIs, background jobs, and production services.
The book moves from strong Python foundations to full system design. It covers typed data flow, functions, objects, iterators, generators, concurrency, profiling, packaging, CLI tools, pipelines, structured outputs, prompt assets, retrieval systems, agents, and production operations such as logging, observability, security, and deployment.
With worked examples, diagrams, a running document-assistant case study, milestone pages, review questions, quick-reference material, and a full capstone project, the book builds both Python fluency and engineering judgment. By the end, readers will be ready to design, build, test, deploy, and improve real Python-based AI systems.
What You Will Learn
- Design clear data records, schemas, and contracts for AI workflows
- Write reliable functions, decorators, classes, and dataclasses for maintainable systems
- Use iterators, generators, context managers, concurrency, and async workflows effectively
- Measure and improve performance with Python profiling and memory tools
- Build dependable CLI tools, batch jobs, and pipeline workflows
- Package Python projects cleanly with modern project metadata and environment isolation
- Validate structured outputs, tool inputs, and model-facing boundaries
- Design prompt assets, retrieval pipelines, embeddings workflows, and agent-style control loops
- Add logging, metrics, traces, evaluation datasets, and release checks to AI systems
- Deploy and operate production AI services with queues, workers, caches, persistence, and recovery paths
Who This Book Is For
This book is for software engineers, backend developers, platform engineers, Python developers, and AI practitioners who want to build real systems with Python. It is a good fit for readers working on AI-enabled applications, retrieval systems, automation workflows, internal tools, or production services.
It is not an introductory Python book. Readers should already be comfortable with basic Python syntax and core programming concepts.
Table of Contents
- Python Rules at System Boundaries
- Types, Records, and Data Flow in AI Systems
- Tooling, Testing, and Repeatable AI Systems
- Functions, Closures, Decorators, and Small Workflows in AI Systems
- Classes, Dataclasses, and Clear Records in AI Systems
- Iterators, Generators, and Context Managers in AI Systems
- Concurrency, Async Work, and Bounded Overlap in AI Systems
- Performance, Memory, and Profiling in AI Systems
- Arrays, DataFrames, and File Formats in AI Systems
- Packaging and Environment Isolation in AI Systems
- CLI Tools and Batch Jobs in AI Systems
- Data Pipelines and External APIs in AI Systems
- Prompt Files, Templates, and Versions in AI Systems
- Structured Outputs, Validation, and Guardrails in AI Systems
- Evaluation, Logging, and Observability in AI Systems
- Configuration, Secrets, and Deployment in AI Systems
- Reliability, Security, and Failure Handling in AI Systems
- Embeddings, Retrieval, and Search in AI Systems
- Agents, Tools, and Workflow Control in AI Systems
- Production Architecture in AI Systems
- Capstone: Building an AI Document Assistant
