Buch, Englisch, 173 Seiten, Hardback, Format (B × H): 190 mm x 235 mm
Buch, Englisch, 173 Seiten, Hardback, Format (B × H): 190 mm x 235 mm
Reihe: Synthesis Lectures on Data Management
ISBN: 978-1-68173-498-9
Verlag: Morgan & Claypool Publishers
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.
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
- 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