Edité par Packt Publishing, Limited, 2023
ISBN 10 : 1837631964 ISBN 13 : 9781837631964
Langue: anglais
Vendeur : Better World Books, Mishawaka, IN, Etats-Unis
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Ajouter au panierEtat : Very Good. Used book that is in excellent condition. May show signs of wear or have minor defects.
Edité par Packt Publishing, Limited, 2023
ISBN 10 : 1837631964 ISBN 13 : 9781837631964
Langue: anglais
Vendeur : Better World Books, Mishawaka, IN, Etats-Unis
EUR 27,13
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Ajouter au panierEtat : Very Good. Former library book; may include library markings. Used book that is in excellent condition. May show signs of wear or have minor defects.
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EUR 27,33
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Ajouter au panierpaperback. Etat : New. Well packaged and promptly shipped from California. Partnered with Friends of the Library since 2010.
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Ajouter au panierPaperback. Etat : Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less 1.06.
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Edité par Packt Publishing 8/31/2023, 2023
ISBN 10 : 1837631964 ISBN 13 : 9781837631964
Langue: anglais
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EUR 49,51
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Ajouter au panierPaperback or Softback. Etat : New. Machine Learning Engineering with Python - Second Edition: Manage the lifecycle of machine learning models using MLOps with practical examples 1.74. Book.
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Vendeur : Russell Books, Victoria, BC, Canada
EUR 54,67
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Ajouter au panierpaperback. Etat : New. 2nd ed. Special order direct from the distributor.
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
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Vendeur : Russell Books, Victoria, BC, Canada
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Ajouter au panierPaperback. Etat : New. Special order direct from the distributor.
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
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Vendeur : La Casa de los Libros, Castellgali, BARCE, Espagne
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Edité par Packt Publishing 2021-11-05, 2021
ISBN 10 : 1801079250 ISBN 13 : 9781801079259
Langue: anglais
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Ajouter au panierEtat : New. Machine learning engineering is an in-demand skill set, and it can be difficult to find a helpful guide on the topic. This book will help you solve business problems by addressing the pain points in creating standardized pipelines for taking proof-of-concep.
Vendeur : Mispah books, Redhill, SURRE, Royaume-Uni
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Ajouter au panierPAP. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Edité par Packt Publishing Limited, 2021
ISBN 10 : 1801079250 ISBN 13 : 9781801079259
Langue: anglais
Vendeur : PBShop.store US, Wood Dale, IL, Etats-Unis
EUR 60,56
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Ajouter au panierPAP. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Ajouter au panierPAP. Etat : New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Edité par Packt Publishing Limited, 2021
ISBN 10 : 1801079250 ISBN 13 : 9781801079259
Langue: anglais
Vendeur : PBShop.store UK, Fairford, GLOS, Royaume-Uni
EUR 58,47
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Ajouter au panierPAP. Etat : New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Edité par Packt Publishing Limited, 2021
ISBN 10 : 1801079250 ISBN 13 : 9781801079259
Langue: anglais
Vendeur : THE SAINT BOOKSTORE, Southport, Royaume-Uni
EUR 62,96
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Ajouter au panierPaperback / softback. Etat : New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 100.
Edité par Packt Publishing Limited, 2023
ISBN 10 : 1837631964 ISBN 13 : 9781837631964
Langue: anglais
Vendeur : THE SAINT BOOKSTORE, Southport, Royaume-Uni
EUR 58,14
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Ajouter au panierPaperback / softback. Etat : New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 526.
Vendeur : Biblios, Frankfurt am main, HESSE, Allemagne
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Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Ajouter au panierTaschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Transform your machine learning projects into successful deployments with this practical guide on how to build and scale solutions that solve real-world problemsIncludes a new chapter on generative AI and large language models (LLMs) and building a pipeline that leverages LLMs using LangChainKey FeaturesThis second edition delves deeper into key machine learning topics, CI/CD, and system designExplore core MLOps practices, such as model management and performance monitoringBuild end-to-end examples of deployable ML microservices and pipelines using AWS and open-source toolsBook DescriptionThe 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.What you will learnPlan and manage end-to-end ML development projectsExplore deep learning, LLMs, and LLMOps to leverage generative AIUse Python to package your ML tools and scale up your solutionsGet to grips with Apache Spark, Kubernetes, and RayBuild and run ML pipelines with Apache Airflow, ZenML, and KubeflowDetect drift and build retraining mechanisms into your solutionsImprove error handling with control flows and vulnerability scanningHost and build ML microservices and batch processes running on AWSWho this book is forThis book is designed for MLOps and ML engineers, data scientists, and software developers who want to build robust solutions that use machine learning to solve real-world problems. If you're not a developer but want to manage or understand the product lifecycle of these systems, you'll also find this book useful. It assumes a basic knowledge of machine learning concepts and intermediate programming experience in Python. With its focus on practical skills and real-world examples, this book is an essential resource for anyone looking to advance their machine learning engineering career.Table of ContentsIntroduction to ML EngineeringThe Machine Learning Development ProcessFrom Model to Model FactoryPackaging UpDeployment Patterns and ToolsScaling UpDeep Learning, Generative AI, and LLMOpsBuilding an Example ML MicroserviceBuilding an Extract, Transform, Machine Learning Use Case.
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
EUR 76,91
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Ajouter au panierTaschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Supercharge the value of your machine learning models by building scalable and robust solutions that can serve them in production environmentsKey FeaturesExplore hyperparameter optimization and model management toolsLearn object-oriented programming and functional programming in Python to build your own ML libraries and packagesExplore key ML engineering patterns like microservices and the Extract Transform Machine Learn (ETML) pattern with use casesBook DescriptionMachine learning engineering is a thriving discipline at the interface of software development and machine learning. This book will help developers working with machine learning and Python to put their knowledge to work and create high-quality machine learning products and services.Machine Learning Engineering with Python takes a hands-on approach to help you get to grips with essential technical concepts, implementation patterns, and development methodologies to have you up and running in no time. You'll begin by understanding key steps of the machine learning development life cycle before moving on to practical illustrations and getting to grips with building and deploying robust machine learning solutions. As you advance, you'll explore how to create your own toolsets for training and deployment across all your projects in a consistent way. The book will also help you get hands-on with deployment architectures and discover methods for scaling up your solutions while building a solid understanding of how to use cloud-based tools effectively. Finally, you'll work through examples to help you solve typical business problems.By the end of this book, you'll be able to build end-to-end machine learning services using a variety of techniques and design your own processes for consistently performant machine learning engineering.What you will learnFind out what an effective ML engineering process looks likeUncover options for automating training and deployment and learn how to use themDiscover how to build your own wrapper libraries for encapsulating your data science and machine learning logic and solutionsUnderstand what aspects of software engineering you can bring to machine learningGain insights into adapting software engineering for machine learning using appropriate cloud technologiesPerform hyperparameter tuning in a relatively automated wayWho this book is forThis book is for machine learning engineers, data scientists, and software developers who want to build robust software solutions with machine learning components. If you're someone who manages or wants to understand the production life cycle of these systems, you'll find this book useful. Intermediate-level knowledge of Python is necessary.Table of ContentsIntroduction to ML EngineeringThe Machine Learning Development ProcessFrom Model to Model FactoryPackaging UpDeployment Patterns and ToolsScaling UpBuilding an Example ML MicroserviceBuilding an Extract Transform Machine Learning Use Case.