04176nam 22006855 450 991025457090332120200703011225.09781484230121148423012410.1007/978-1-4842-3012-1(CKB)3710000001632764(DE-He213)978-1-4842-3012-1(MiAaPQ)EBC4984516(CaSebORM)9781484230121(PPN)203853997(OCoLC)1077473855(OCoLC)on1077473855(EXLCZ)99371000000163276420170823d2017 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierDocker for Data Science Building Scalable and Extensible Data Infrastructure Around the Jupyter Notebook Server /by Joshua Cook1st ed. 2017.Berkeley, CA :Apress :Imprint: Apress,2017.1 online resource (XXI, 257 p. 97 illus., 76 illus. in color.) Includes index.9781484230114 1484230116 Includes bibliographical references.Chapter 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.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 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.Big dataArtificial intelligenceOpen source softwareComputer programmingPython (Computer program language)Big Datahttps://scigraph.springernature.com/ontologies/product-market-codes/I29120Artificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Open Sourcehttps://scigraph.springernature.com/ontologies/product-market-codes/I29090Pythonhttps://scigraph.springernature.com/ontologies/product-market-codes/I29080Big data.Artificial intelligence.Open source software.Computer programming.Python (Computer program language)Big Data.Artificial Intelligence.Open Source.Python.005.1Cook Joshuaauthttp://id.loc.gov/vocabulary/relators/aut974213UMIUMIBOOK9910254570903321Docker for Data Science2217901UNINA