Data Management in Machine Learning Systems (Record no. 84938)
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| 000 -LEADER | |
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| fixed length control field | 03894nam a22005295i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-031-01869-5 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240730163737.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 220601s2019 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783031018695 |
| -- | 978-3-031-01869-5 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 004.6 |
| 100 1# - AUTHOR NAME | |
| Author | Boehm, Matthias. |
| 245 10 - TITLE STATEMENT | |
| Title | Data Management in Machine Learning Systems |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2019. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XV, 157 p. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Synthesis Lectures on Data Management, |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Preface -- 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 ## - SUMMARY, ETC. | |
| Summary, etc | Large-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. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Kumar, Arun. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Yang, Jun. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-3-031-01869-5 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
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| -- | Cham : |
| -- | Springer International Publishing : |
| -- | Imprint: Springer, |
| -- | 2019. |
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| -- | computer |
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| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
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| 347 ## - | |
| -- | text file |
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| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer networks . |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data structures (Computer science). |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Information theory. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer Communication Networks. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data Structures and Information Theory. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 2153-5426 |
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| -- | ZDB-2-SXSC |
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