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Paperback. Zustand: new. Paperback. Key FeaturesAdvanced 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 str…ong 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 LearnDesign clear data records, schemas, and contracts for AI workflowsWrite reliable functions, decorators, classes, and dataclasses for maintainable systemsUse iterators, generators, context managers, concurrency, and async workflows effectivelyMeasure and improve performance with Python profiling and memory toolsBuild dependable CLI tools, batch jobs, and pipeline workflowsPackage Python projects cleanly with modern project metadata and environment isolationValidate structured outputs, tool inputs, and model-facing boundariesDesign prompt assets, retrieval pipelines, embeddings workflows, and agent-style control loopsAdd logging, metrics, traces, evaluation datasets, and release checks to AI systemsDeploy and operate production AI services with queues, workers, caches, persistence, and recovery pathsWho This Book Is ForThis 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 ContentsPython Rules at System BoundariesTypes, Records, and Data Flow in AI SystemsTooling, Testing, and Repeatable AI SystemsFunctions, Closures, Decorators, and Small Workflows in AI SystemsClasses, Dataclasses, and Clear Records in AI SystemsIterators, Generators, and Context Managers in AI SystemsConcurrency, Async Work, and Bounded Overlap in AI SystemsPerformance, Memory, and Profiling in AI SystemsArrays, DataFrames, and File Formats in AI SystemsPackaging and Environment Isolation in AI SystemsCLI Tools and Batch Jobs in AI SystemsData Pipelines and External APIs in AI SystemsPrompt Files, Templates, and Versions in AI SystemsStructured Outputs, Validation, and Guardrails in AI SystemsEvaluation, Logging, and Observability in AI SystemsConfiguration, Secrets, and Deployment in AI SystemsReliability, Security, and Failure Handling in AI SystemsEmbeddings, Retrieval, and Search in AI SystemsAgents, Tools, and Workflow Control in AI SystemsProduction Architecture in AI SystemsCapstone: Building an AI Document Assistant This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Paperback. Zustand: new. Paperback. Key FeaturesAdvanced 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 str…ong 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 LearnDesign clear data records, schemas, and contracts for AI workflowsWrite reliable functions, decorators, classes, and dataclasses for maintainable systemsUse iterators, generators, context managers, concurrency, and async workflows effectivelyMeasure and improve performance with Python profiling and memory toolsBuild dependable CLI tools, batch jobs, and pipeline workflowsPackage Python projects cleanly with modern project metadata and environment isolationValidate structured outputs, tool inputs, and model-facing boundariesDesign prompt assets, retrieval pipelines, embeddings workflows, and agent-style control loopsAdd logging, metrics, traces, evaluation datasets, and release checks to AI systemsDeploy and operate production AI services with queues, workers, caches, persistence, and recovery pathsWho This Book Is ForThis 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 ContentsPython Rules at System BoundariesTypes, Records, and Data Flow in AI SystemsTooling, Testing, and Repeatable AI SystemsFunctions, Closures, Decorators, and Small Workflows in AI SystemsClasses, Dataclasses, and Clear Records in AI SystemsIterators, Generators, and Context Managers in AI SystemsConcurrency, Async Work, and Bounded Overlap in AI SystemsPerformance, Memory, and Profiling in AI SystemsArrays, DataFrames, and File Formats in AI SystemsPackaging and Environment Isolation in AI SystemsCLI Tools and Batch Jobs in AI SystemsData Pipelines and External APIs in AI SystemsPrompt Files, Templates, and Versions in AI SystemsStructured Outputs, Validation, and Guardrails in AI SystemsEvaluation, Logging, and Observability in AI SystemsConfiguration, Secrets, and Deployment in AI SystemsReliability, Security, and Failure Handling in AI SystemsEmbeddings, Retrieval, and Search in AI SystemsAgents, Tools, and Workflow Control in AI SystemsProduction Architecture in AI SystemsCapstone: Building an AI Document Assistant This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
