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Titlebook: Data Management in Machine Learning Systems; Matthias Boehm,Arun Kumar,Jun Yang Book 2019 Springer Nature Switzerland AG 2019

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書目名稱Data Management in Machine Learning Systems
編輯Matthias Boehm,Arun Kumar,Jun Yang
視頻videohttp://file.papertrans.cn/263/262870/262870.mp4
叢書名稱Synthesis Lectures on Data Management
圖書封面Titlebook: Data Management in Machine Learning Systems;  Matthias Boehm,Arun Kumar,Jun Yang Book 2019 Springer Nature Switzerland AG 2019
描述.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, an
出版日期Book 2019
版次1
doihttps://doi.org/10.1007/978-3-031-01869-5
isbn_softcover978-3-031-00741-5
isbn_ebook978-3-031-01869-5Series ISSN 2153-5418 Series E-ISSN 2153-5426
issn_series 2153-5418
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

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Marisol J. Voncken,Susan M. B?gelsnal model and query language seem to be a poor fit with ML, as most ML algorithms look very different from and oftentimes far more complicated than database queries. Thus, database systems have traditionally served as a data store for ML; the ML algorithm would pull the data out from the database, t
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Cognitive Psychology: What Is In The Box?tems apply a broad range of rewrites and optimization techniques to improve the efficiency of ML programs. In this chapter, we first categorize existing systems according to their optimization scope, survey important classes of logical and physical rewrites, and also discuss means of adapting execut
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Evolution from Linear to Systems Thinkingent data access in ML systems. These techniques bear strong similarity with corresponding data access methods in database systems, with the difference of focusing on dense and sparse matrices or tensors, as well as specific access patterns of ML workloads. In this chapter, we survey existing techniq
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