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Professional Python for AI Engineering: Developing Advanced Solutions in Data Science and Deep Learning - Couverture souple
Livre 1 sur 4: The Enterprise Data Science Mastery Series
Synopsis
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 projects
- Optimize memory usage, algorithms, data loading, and expensive computations
- Process massive datasets using multiprocessing, Dask, Polars, and out-of-core techniques
- Apply architectural patterns to create modular and maintainable machine learning systems
- Structure PyTorch and TensorFlow projects for production
- Optimize GPU utilization, mixed-precision training, distributed training, and hyperparameters
- Build scalable and fault-tolerant model training pipelines
- Serialize and deploy models with ONNX, TorchScript, REST APIs, gRPC, and containers
- Test, profile, debug, monitor, and continuously improve AI codebases
- Connect data pipelines, feature stores, retraining workflows, telemetry, and security into end-to-end AI engineering systems
Take your Python skills beyond scripting and into professional AI engineering.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
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Professional Python for AI Engineering (Paperback)
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Paperback. Etat : 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. N° de réf. du vendeur 9798170536788
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Professional Python for AI Engineering (Paperback)
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Paperback. Etat : 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. N° de réf. du vendeur 9798170536788
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Professional Python for AI Engineering : Developing Advanced Solutions in Data Science and Deep Learning
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Taschenbuch. Etat : Neu. Neuware - 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 projects- Optimize memory usage, algorithms, data loading, and expensive computations- Process massive datasets using multiprocessing, Dask, Polars, and out-of-core techniques- Apply architectural patterns to create modular and maintainable machine learning systems- Structure PyTorch and TensorFlow projects for production- Optimize GPU utilization, mixed-precision training, distributed training, and hyperparameters- Build scalable and fault-tolerant model training pipelines- Serialize and deploy models with ONNX, TorchScript, REST APIs, gRPC, and containers- Test, profile, debug, monitor, and continuously improve AI codebases- Connect 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. N° de réf. du vendeur 9798170536788
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