Isbn: 9798170536788 - professional python for ai engineering: developing advanced solutions in data science and deep learning: 1 (the enterprise data science mastery series) (5 resultados)

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  • Idioma: Inglés

    Editorial: WENDE, 2026

    9798170536788

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    Librería: PBShop.store UK, Fairford, GLOS, Reino UnidoPBShop.store UK

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    PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

  • Idioma: Inglés

    Editorial: Independently Published Aug 2026, 2026

    9798170536788

    Serie: Libro 1 de 4 - The Enterprise Data Science Mastery Series

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    Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH

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    Taschenbuch. Condición: Neu. Neuware.

  • Idioma: Inglés

    Editorial: Independently published, 2026

    9798170536788

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    Librería: California Books, Miami, FL, Estados Unidos de AmericaCalifornia Books

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  • Idioma: Inglés

    Editorial: Independently Published, 2026

    9798170536788

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    Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de AmericaGrand Eagle Retail

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    Condición: Nuevo

    EUR 22,15

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    Cantidad disponible: 1 disponibles

    Paperback. Condición: new. Paperback. What does it take to move from writing Python scripts to engineering production-ready AI systems? Professional Python for AI Engineering is a practical guide for software engineers and data scientists who want to use Python at a professional level for data science, machine learning, and deep learning applications.Rather than focusing on basic Python syntax, this book explores the advanced techniques needed to design, optimize, test, deploy, and maintain robust AI software.You'll explore how to: Build reproducible Python environments and manage dependencies for AI projectsOptimize memory usage, algorithms, data loading, and expensive computationsProcess massive datasets using multiprocessing, Dask, Polars, and out-of-core techniquesApply architectural patterns to create modular and maintainable machine learning systemsStructure PyTorch and TensorFlow projects for productionOptimize GPU utilization, mixed-precision training, distributed training, and hyperparametersBuild scalable and fault-tolerant model training pipelinesSerialize and deploy models with ONNX, TorchScript, REST APIs, gRPC, and containersTest, profile, debug, monitor, and continuously improve AI codebasesConnect data pipelines, feature stores, retraining workflows, telemetry, and security into end-to-end AI engineering systemsWhether you are developing machine learning pipelines, deep learning applications, or production AI infrastructure, this book focuses on the engineering practices that help turn experimental code into reliable software.Take your Python skills beyond scripting and into professional AI engineering. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

  • Idioma: Inglés

    Editorial: Independently Published, 2026

    9798170536788

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    Librería: CitiRetail, Stevenage, Reino UnidoCitiRetail

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    Condición: Nuevo

    EUR 23,31

    Envío por EUR 42,97 
    Se envía de Reino Unido a Estados Unidos de America

    Cantidad disponible: 1 disponibles

    Paperback. Condición: new. Paperback. What does it take to move from writing Python scripts to engineering production-ready AI systems? Professional Python for AI Engineering is a practical guide for software engineers and data scientists who want to use Python at a professional level for data science, machine learning, and deep learning applications.Rather than focusing on basic Python syntax, this book explores the advanced techniques needed to design, optimize, test, deploy, and maintain robust AI software.You'll explore how to: Build reproducible Python environments and manage dependencies for AI projectsOptimize memory usage, algorithms, data loading, and expensive computationsProcess massive datasets using multiprocessing, Dask, Polars, and out-of-core techniquesApply architectural patterns to create modular and maintainable machine learning systemsStructure PyTorch and TensorFlow projects for productionOptimize GPU utilization, mixed-precision training, distributed training, and hyperparametersBuild scalable and fault-tolerant model training pipelinesSerialize and deploy models with ONNX, TorchScript, REST APIs, gRPC, and containersTest, profile, debug, monitor, and continuously improve AI codebasesConnect data pipelines, feature stores, retraining workflows, telemetry, and security into end-to-end AI engineering systemsWhether you are developing machine learning pipelines, deep learning applications, or production AI infrastructure, this book focuses on the engineering practices that help turn experimental code into reliable software.Take your Python skills beyond scripting and into professional AI engineering. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…