Name: Data Management in Machine Learning Systems (en Inglés)
Price: 65160.00 CLP
Availability: InStock
Author: Boehm, Matthias ; Kumar, Arun ; Yang, Jun
ISBN: 9783031007415

portada Data Management in Machine Learning Systems (en Inglés)

Formato

Libro Físico

Autor

Editorial

Idioma

Inglés

N° páginas

157

Encuadernación

Tapa Blanda

Dimensiones

23.5 x 19.1 x 1.0 cm

Peso

0.31 kg.

ISBN13

9783031007415

N° edición

Categorías

Ciencia y análisis de datos
Análisis y captura de datos
Inteligencia artificial

Data Management in Machine Learning Systems (en Inglés)

Arun Kumar (Autor) · Jun Yang (Autor) · Matthias Boehm (Autor) · Springer · Tapa Blanda

Libro Nuevo

$ 65.160

$ 130.310

Ahorras: $ 65.150

50% descuento

Envío normal

Origen: Estados Unidos (Costos de importación incluídos en el precio)

Se enviará desde nuestra bodega entre el Viernes 12 de Julio y el Miércoles 24 de Julio.

Lo recibirás en cualquier lugar de Chile entre 1 y 3 días hábiles luego del envío.

Reseña del libro "Data Management in Machine Learning Systems (en Inglés)"

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.