000 | 03894nam a22005295i 4500 | ||
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001 | 978-3-031-01869-5 | ||
003 | DE-He213 | ||
005 | 20240730163737.0 | ||
007 | cr nn 008mamaa | ||
008 | 220601s2019 sz | s |||| 0|eng d | ||
020 |
_a9783031018695 _9978-3-031-01869-5 |
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024 | 7 |
_a10.1007/978-3-031-01869-5 _2doi |
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050 | 4 | _aTK5105.5-5105.9 | |
072 | 7 |
_aUKN _2bicssc |
|
072 | 7 |
_aCOM043000 _2bisacsh |
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072 | 7 |
_aUKN _2thema |
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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. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
||
338 |
_aonline resource _bcr _2rdacarrier |
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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 |
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650 | 0 |
_aData structures (Computer science). _98188 |
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650 | 0 |
_aInformation theory. _914256 |
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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 |
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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 _d84938 |