Data Management in Machine Learning Systems

Name: Data Management in Machine Learning Systems
Brand: Springer International Publishing AG
SKU: 9783031007415
Price: 1120 CZK
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Autor: Jun Yang, Arun Kumar

Jazyk:

Vazba: Brožovaná

Vydavatel: Springer International Publishing AG

Dostupnost: Skladem u dodavatele

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Large-scale data analytics using machine learning (ML) underpins many modern data-driven application...

Informace o knize

Autor

Jun Yang, Arun Kumar

Jazyk

Angličtina

Vazba

Kniha - Brožovaná

Vydáno

2019

Stránek

157

EAN

9783031007415

ISBN

3031007417

Enbook ID

39298299

Vydavatel

Springer International Publishing AG

Hmotnost

344

Rozměry

191 x 235 x 10

Kompletní popis

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.

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Data Management in Machine Learning Systems

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