SystemDS: A Declarative Machine Learning System for the End-to-End Data Science Lifecycle
Summary: SystemDS is an open-source declarative ML system that unifies the end-to-end data science lifecycle—data integration, cleaning, preparation, local/distributed/federated training, debugging, and serving—via a stack of language abstractions. It targets lifecycle-wide optimization to eliminate boundary crossing between data engineering and modeling, building on SystemML lessons to support heterogeneous data and diverse user expertise. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Matthias Boehm
- 2. Iulian Antonov
- 3. Sebastian Baunsgaard
- 4. Mark Dokter
- 5. Robert Ginthör
- 6. Kevin Innerebner
- 7. Florijan Klezin
- 8. Stefanie Lindstaedt
- 9. Arnab Phani
- 10. Benjamin Rath
- 11. Berthold Reinwald
- 12. Shafaq Siddiqi
- 13. Sebastian Benjamin Wrede
Incoming Citations (Sorted by Pagerank)
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| 543 | MLbase: A Distributed Machine-learning System | 2013 | CIDR | 0.00020526854 |
| 4,003 | Data Platform for Machine Learning | 2019 | SIGMOD | 6.54347e-05 |
| 13,171 | Reimagining Deep Learning Systems Through the Lens of Data Systems | 2024 | VLDB | - |
| 557 | SystemML: Declarative Machine Learning on Spark | 2016 | VLDB | 0.00020197988 |