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008 220518s2022 enk o 000 0 eng d
040 _aUKMGB
_beng
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_dOCLCF
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015 _aGBC293022
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020 _a1803231653
020 _a9781803231655
_q(electronic bk.)
020 _z9781803241807 (pbk.)
035 _a3291290
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035 _a(OCoLC)1328022045
037 _a9781803231655
_bPackt Publishing Pvt. Ltd
037 _a9781803241807
_bO'Reilly Media
050 4 _aQ325.5
082 0 4 _a006.3/1
_223/eng/20220706
049 _aMAIN
100 1 _aMasood, Faisal,
_eauthor.
_920092
245 1 0 _aMachine learning on Kubernetes :
_ba practical handbook for building and using a complete open source machine learning platform on Kubernetes /
_cFaisal Masood, Ross Brigoli.
264 1 _aBirmingham :
_bPackt Publishing,
_c2022.
300 _a1 online resource
336 _atext
_2rdacontent
337 _acomputer
_2rdamedia
338 _aonline resource
_2rdacarrier
588 _aDescription based on CIP data; resource not viewed.
520 _aBuild a Kubernetes-based self-serving, agile data science and machine learning ecosystem for your organization using reliable and secure open source technologies Key Features Build a complete machine learning platform on Kubernetes Improve the agility and velocity of your team by adopting the self-service capabilities of the platform Reduce time-to-market by automating data pipelines and model training and deployment Book Description MLOps is an emerging field that aims to bring repeatability, automation, and standardization of the software engineering domain to data science and machine learning engineering. By implementing MLOps with Kubernetes, data scientists, IT professionals, and data engineers can collaborate and build machine learning solutions that deliver business value for their organization. You'll begin by understanding the different components of a machine learning project. Then, you'll design and build a practical end-to-end machine learning project using open source software. As you progress, you'll understand the basics of MLOps and the value it can bring to machine learning projects. You will also gain experience in building, configuring, and using an open source, containerized machine learning platform. In later chapters, you will prepare data, build and deploy machine learning models, and automate workflow tasks using the same platform. Finally, the exercises in this book will help you get hands-on experience in Kubernetes and open source tools, such as JupyterHub, MLflow, and Airflow. By the end of this book, you'll have learned how to effectively build, train, and deploy a machine learning model using the machine learning platform you built. What you will learn Understand the different stages of a machine learning project Use open source software to build a machine learning platform on Kubernetes Implement a complete ML project using the machine learning platform presented in this book Improve on your organization's collaborative journey toward machine learning Discover how to use the platform as a data engineer, ML engineer, or data scientist Find out how to apply machine learning to solve real business problems Who this book is for This book is for data scientists, data engineers, IT platform owners, AI product owners, and data architects who want to build their own platform for ML development. Although this book starts with the basics, a solid understanding of Python and Kubernetes, along with knowledge of the basic concepts of data science and data engineering will help you grasp the topics covered in this book in a better way.
590 _aAdded to collection customer.56279.3
650 0 _aMachine learning.
_92890
650 0 _aOpen source software.
650 7 _aMachine learning.
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650 7 _aOpen source software.
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655 4 _aElectronic books.
_93907
700 1 _aBrigoli, Ross,
_eauthor.
_920093
776 0 8 _iPrint version:
_z9781803241807
856 4 0 _3EBSCOhost
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