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Docker for Data Science: Building Scalable and Extensible Data Infrastructure Around the Jupyter Notebook Server - Couverture souple
Synopsis
Teaches Docker principles with practical examples
Covers high-performance interactive computing with Jupyter
Presents a unique development method geared toward interactive computing
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À propos de l?auteur
Joshua Cook is a mathematician. He writes code in Bash, C, and Python and has done pure and applied computational work in geo-spatial predictive modeling, quantum mechanics, semantic search, and artificial intelligence. He also has 10 years experience teaching mathematics at the secondary and post-secondary level. His research interests lie in high-performance computing, interactive computing, feature extraction, and reinforcement learning. He is always willing to discuss orthogonality or to explain why Fortran is the language of the future over a warm or cold beverage.
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Docker for Data Science: Building Scalable and Extensible Data Infrastructure Around the Jupyter Notebook Server
Vendeur : Brit Books, Milton Keynes, Royaume-Uni
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Paperback. Etat : Used; Good. ***Simply Brit*** Welcome to our online used book store, where affordability meets great quality. Dive into a world of captivating reads without breaking the bank. We take pride in offering a wide selection of used books, from classics to hidden gems, ensuring there is something for every literary palate. All orders are shipped within 24 hours and our lightning fast-delivery within 48 hours coupled with our prompt customer service ensures a smooth journey from ordering to delivery. Discover the joy of reading with us, your trusted source for affordable books that do not compromise on quality. N° de réf. du vendeur 3452423
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Docker for Data Science: Building Scalable and Extensible Data Infrastructure Around the Jupyter Notebook Server
Vendeur : Books From California, Simi Valley, CA, Etats-Unis
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Paperback. Etat : Very Good. Cover and edges may have some wear. N° de réf. du vendeur mon0003691242
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Docker for Data Science
impression à la demandeVendeur : moluna, Greven, Allemagne
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Joshua Cook is a mathematician. He writes code in Bash, C, and Python and has done pure and applied computational work in geo-spatial predictive modeling, quantum mechanics, semantic search, and artificial intelligence. He also has 10 years experienc. N° de réf. du vendeur 154279572
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Docker for Data Science: Building Scalable and Extensible Data Infrastructure Around the Jupyter Notebook Server
Edité par
Apress, 2017
ISBN 10 : 1484230116
ISBN 13 : 9781484230114
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Couverture souple
Vendeur : SecondSale, Montgomery, IL, Etats-Unis
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Etat : Good. Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc. N° de réf. du vendeur 00046228659
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Docker for Data Science: Building Scalable and Extensible Data Infrastructure Around the Jupyter Notebook Server
Vendeur : California Books, Miami, FL, Etats-Unis
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Etat : New. N° de réf. du vendeur I-9781484230114
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Docker for Data Science: Building Scalable and Extensible Data Infrastructure Around the Jupyter Notebook Server
Vendeur : Toscana Books, AUSTIN, TX, Etats-Unis
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Paperback. Etat : new. Excellent Condition.Excels in customer satisfaction, prompt replies, and quality checks. N° de réf. du vendeur Scanned1484230116
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Docker for Data Science
impression à la demandeVendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Learn Docker 'infrastructure as code' technology to define a system for performing standard but non-trivial data tasks on medium- to large-scale data sets, using Jupyter as the master controller.It is not uncommon for a real-world data set to fail to be easily managed. The set may not fit well into access memory or may require prohibitively long processing. These are significant challenges to skilled software engineers and they can render the standard Jupyter system unusable. As a solution to this problem, Docker for Data Science proposes using Docker. You will learn how to use existing pre-compiled public images created by the major open-source technologies-Python, Jupyter, Postgres-as well as using the Dockerfile to extend these images to suit your specific purposes. The Docker-Compose technology is examined and you will learn how it can be used to build a linked system with Python churning data behind the scenes and Jupyter managing these background tasks. Best practices in using existing images are explored as well as developing your own images to deploy state-of-the-art machine learning and optimization algorithms.What You'll Learn Master interactive development using the Jupyter platformRun and build Docker containers from scratch and from publicly available open-source imagesWrite infrastructure as code using the docker-compose tool and its docker-compose.yml file typeDeploy a multi-service data science application across a cloud-based systemWho This Book Is ForData scientists, machine learning engineers, artificial intelligence researchers, Kagglers, and software developers 284 pp. Englisch. N° de réf. du vendeur 9781484230114
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Docker for Data Science : Building Scalable and Extensible Data Infrastructure Around the Jupyter Notebook Server
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Docker for Data Science: Building Scalable and Extensible Data Infrastructure Around the Jupyter Notebook Server
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
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Etat : New. In English. N° de réf. du vendeur ria9781484230114_new
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Docker for Data Science : Building Scalable and Extensible Data Infrastructure Around the Jupyter Notebook Server
impression à la demandeVendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Learn Docker 'infrastructure as code' technology to define a system for performing standard but non-trivial data tasks on medium- to large-scale data sets, using Jupyter as the master controller.It is not uncommon for a real-world data set to fail to be easily managed. The set may not fit well into access memory or may require prohibitively long processing. These are significant challenges to skilled software engineers and they can render the standard Jupyter system unusable. As a solution to this problem, Docker for Data Science proposes using Docker. You will learn how to use existing pre-compiled public images created by the major open-source technologies-Python, Jupyter, Postgres-as well as using the Dockerfile to extend these images to suit your specific purposes. The Docker-Compose technology is examined and you will learn how it can be used to build a linked system with Python churning data behind the scenesand Jupyter managing these background tasks. Best practices in using existing images are explored as well as developing your own images to deploy state-of-the-art machine learning and optimization algorithms.What You'll Learn Master interactive development using the Jupyter platformRun and build Docker containers from scratch and from publicly available open-source imagesWrite infrastructure as code using the docker-compose tool and its docker-compose.yml file typeDeploy a multi-service data science application across a cloud-based systemWho This Book Is ForData scientists, machine learning engineers, artificial intelligence researchers, Kagglers, and software developers. N° de réf. du vendeur 9781484230114
Quantité disponible : 1 disponible(s)