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Data Management in Machine Learning Systems (Paperback)
Loot Price: R1,533
Discovery Miles 15 330
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Data Management in Machine Learning Systems (Paperback)
Series: Synthesis Lectures on Data Management
Expected to ship within 10 - 15 working days
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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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