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010   $a9781484230121
010   $a1484230124
024 7 $a10.1007/978-1-4842-3012-1
035   $a(CKB)3710000001632764
035   $a(DE-He213)978-1-4842-3012-1
035   $a(MiAaPQ)EBC4984516
035   $a(CaSebORM)9781484230121
035   $a(PPN)203853997
035   $a(OCoLC)1077473855
035   $a(OCoLC)on1077473855
035   $a(EXLCZ)993710000001632764
100   $a20170823d2017      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aDocker for Data Science $eBuilding Scalable and Extensible Data Infrastructure Around the Jupyter Notebook Server /$fby Joshua Cook
205   $a1st ed. 2017.
210  1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2017.
215   $a1 online resource (XXI, 257 p. 97 illus., 76 illus. in color.) 
300   $aIncludes index.
311 08$a9781484230114 
311 08$a1484230116 
320   $aIncludes bibliographical references.
327   $aChapter 1:  Introduction -- Chapter 2:  Docker -- Chapter 3: Interactive Programming -- Chapter 4: Docker Engine -- Chapter 5: The Dockerfile -- Chapter 6: Docker Hub -- Chapter 7: The Opinionated Jupyter Stacks -- Chapter 8: The Data Stores -- Chapter 9: Docker Compose -- Chapter 10: Interactive Development.
330   $aLearn 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 platform Run and build Docker containers from scratch and from publicly available open-source images Write infrastructure as code using the docker-compose tool and its docker-compose.yml file type Deploy a multi-service data science application across a cloud-based system.
606   $aBig data
606   $aArtificial intelligence
606   $aOpen source software
606   $aComputer programming
606   $aPython (Computer program language)
606   $aBig Data$3https://scigraph.springernature.com/ontologies/product-market-codes/I29120
606   $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000
606   $aOpen Source$3https://scigraph.springernature.com/ontologies/product-market-codes/I29090
606   $aPython$3https://scigraph.springernature.com/ontologies/product-market-codes/I29080
615  0$aBig data.
615  0$aArtificial intelligence.
615  0$aOpen source software.
615  0$aComputer programming.
615  0$aPython (Computer program language)
615 14$aBig Data.
615 24$aArtificial Intelligence.
615 24$aOpen Source.
615 24$aPython.
676   $a005.1
700   $aCook$b Joshua$4aut$4http://id.loc.gov/vocabulary/relators/aut$0974213
801  0$bUMI
801  1$bUMI
906   $aBOOK
912   $a9910254570903321
996   $aDocker for Data Science$92217901
997   $aUNINA