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

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Resource Heterogeneity and Elasticity,ve made it easier than ever to access computing resources. For example, a public cloud such as Amazon EC2 allows users to acquire a cluster on demand and pay only for its actual usage. There is a blossoming ecosystem of tools, libraries, and platforms for ML in the cloud and cluster computing settin

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Conclusions,ed systems, high-performance computing, programming languages, and more. Motivated by (1) data-driven applications, (2) data-intensive workload characteristics, and (3) data systems support for ML, we took a data-centric view and reviewed approaches for integrating ML in data systems as well as data

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Execution Strategies, discuss interesting runtime techniques, including special runtime optimizers. Finally, in the context of deep learning, there is a trend toward exploiting accelerators such as GPUs, FPGAs, and custom ASICs for training and scoring, which is another specific hybrid execution strategy.

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