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003			ES-MaUEC
005			20230220161419.0
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007			cr nn 008mamaa
008			220601s2019 sz | s |||| 0|eng d
020			_a9783031018695
024	7		_a10.1007/978-3-031-01869-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2019 EB
100	1		_aBoehm, Matthias _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687054
245	1	0	_aData Management in Machine Learning Systems _cby Matthias Boehm, Arun Kumar, Jun Yang
250			_a1st edition 2019
264		1	_aCham _bSpringer International Publishing _c2019
300			_a1 recurso en línea (XV, 157 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_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.
988			_aSynthesis Collection of Technology_2019
700	1		_aKumar, Arun, _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687055 _d1988-
700			_aYang, Jun _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9100182
776	0	8	_iPrinted edition: _z9783031000966
776	0	8	_iPrinted edition: _z9783031007415
776	0	8	_iPrinted edition: _z9783031029974
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01869-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2023 _dz _esc _zSI