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008 220601s2019 sz | s |||| 0|eng d
020 _a9783031018695
_9978-3-031-01869-5
024 7 _a10.1007/978-3-031-01869-5
_2doi
050 4 _aTK5105.5-5105.9
072 7 _aUKN
_2bicssc
072 7 _aCOM043000
_2bisacsh
072 7 _aUKN
_2thema
082 0 4 _a004.6
_223
100 1 _aBoehm, Matthias.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980320
245 1 0 _aData Management in Machine Learning Systems
_h[electronic resource] /
_cby Matthias Boehm, Arun Kumar, Jun Yang.
250 _a1st ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aXV, 157 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Data Management,
_x2153-5426
505 0 _aPreface -- Acknowledgments -- Introduction -- ML Through Database Queries and UDFs -- Multi-Table ML and Deep Systems Integration -- Rewrites and Optimization -- Execution Strategies -- Data Access Methods -- Resource Heterogeneity and Elasticity -- Systems for ML Lifecycle Tasks -- Conclusions -- Bibliography -- Authors' Biographies.
520 _aLarge-scale data analytics using machine learning (ML) underpins many modern data-driven applications. ML systems provide means of specifying and executing these ML workloads in an efficient and scalable manner. Data management is at the heart of many ML systems due to data-driven application characteristics, data-centric workload characteristics, and system architectures inspired by classical data management techniques. In this book, we follow this data-centric view of ML systems and aim to provide a comprehensive overview of data management in ML systems for the end-to-end data science or ML lifecycle. We review multiple interconnected lines of work: (1) ML support in database (DB) systems, (2) DB-inspired ML systems, and (3) ML lifecycle systems. Covered topics include: in-database analytics via query generation and user-defined functions, factorized and statistical-relational learning; optimizing compilers for ML workloads; execution strategies and hardware accelerators;data access methods such as compression, partitioning and indexing; resource elasticity and cloud markets; as well as systems for data preparation for ML, model selection, model management, model debugging, and model serving. Given the rapidly evolving field, we strive for a balance between an up-to-date survey of ML systems, an overview of the underlying concepts and techniques, as well as pointers to open research questions. Hence, this book might serve as a starting point for both systems researchers and developers.
650 0 _aComputer networks .
_931572
650 0 _aData structures (Computer science).
_98188
650 0 _aInformation theory.
_914256
650 1 4 _aComputer Communication Networks.
_980321
650 2 4 _aData Structures and Information Theory.
_931923
700 1 _aKumar, Arun.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980322
700 1 _aYang, Jun.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980323
710 2 _aSpringerLink (Online service)
_980324
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031000966
776 0 8 _iPrinted edition:
_z9783031007415
776 0 8 _iPrinted edition:
_z9783031029974
830 0 _aSynthesis Lectures on Data Management,
_x2153-5426
_980325
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01869-5
912 _aZDB-2-SXSC
942 _cEBK
999 _c84938
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