Towards Observability for Production Machine Learning Pipelines
Summary: End-to-end observability for production ML pipelines to address post-deployment issues like data shift and silent failures. Proposes a bolt-on data-management architecture enabling detection, diagnosis, and reaction, wrapping existing tools to deliver ML observability. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Shreya Shankar (University of California Berkeley)
- 2. Aditya G. Parameswaran (University of California Berkeley)
BibTeX Citation
@article{shankar_vldb22,
title = {{Towards Observability for Production Machine Learning Pipelines}},
author = {Shankar, Shreya and Parameswaran, Aditya G.},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {13},
pages = {4015--4022},
doi = {10.14778/3565838.3565853},
url = {https://doi.org/10.14778/3565838.3565853},
year = {2022}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,117 | Modyn: Data-Centric Machine Learning Pipeline Orchestration | 2025 | SIGMOD | 5.2263399e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 27 of 27 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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