Machine learning engineering with Python : manage the lifecycle of machine learning models using MLOps with practical examples /

The Second Edition of Machine Learning Engineering with Python is the practical guide that MLOps and ML engineers need to build solutions to real-world problems. It will provide you with the skills you need to stay ahead in this rapidly evolving field. The book takes an examples-based approach to he...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: McMahon, Andrew P. (Autor)
Otros Autores: Polak, Adi (writer of foreword.)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham, UK : Packt Publishing Ltd., 2023.
Edición:Second edition.
Colección:Expert insight.
Temas:
Machine learning.
Python (Computer program language)
Data mining.
Apprentissage automatique.
Python (Langage de programmation)
Exploration de données (Informatique)
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)

MARC

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250 |a Second edition. 
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300 |a 1 online resource (462 pages) :  |b illustrations. 
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500 |a Includes index. 
520 |a The Second Edition of Machine Learning Engineering with Python is the practical guide that MLOps and ML engineers need to build solutions to real-world problems. It will provide you with the skills you need to stay ahead in this rapidly evolving field. The book takes an examples-based approach to help you develop your skills and covers the technical concepts, implementation patterns, and development methodologies you need. You'll explore the key steps of the ML development lifecycle and create your own standardized "model factory" for training and retraining of models. You'll learn to employ concepts like CI/CD and how to detect different types of drift. Get hands-on with the latest in deployment architectures and discover methods for scaling up your solutions. This edition goes deeper in all aspects of ML engineering and MLOps, with emphasis on the latest open-source and cloud-based technologies. This includes a completely revamped approach to advanced pipelining and orchestration techniques. With a new chapter on deep learning, generative AI, and LLMOps, you will learn to use tools like LangChain, PyTorch, and Hugging Face to leverage LLMs for supercharged analysis. You will explore AI assistants like GitHub Copilot to become more productive, then dive deep into the engineering considerations of working with deep learning. 
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650 0 |a Data mining. 
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650 6 |a Python (Langage de programmation) 
650 6 |a Exploration de données (Informatique) 
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