Data management in machine learning systems

Bibliographic Information

Data management in machine learning systems

Matthias Boehm, Arun Kumar, Jun Yang

(Synthesis lectures on data management, #57)

Morgan & Craypool, c2019

  • : pbk

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Note

Includes bibliographical references (p.127-156)

DOI:10.2200/S00895ED1V01Y201901DTM057

Description and Table of Contents

Description

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.

Table of Contents

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

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Details
  • NCID
    BB28119725
  • ISBN
    • 9781681734965
  • Country Code
    us
  • Title Language Code
    eng
  • Text Language Code
    eng
  • Place of Publication
    [San Rafael, Calif.]
  • Pages/Volumes
    xv, 157 p.
  • Size
    24 cm
  • Parent Bibliography ID
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