Managing ML Pipelines: Feature Stores and the Coming Wave of Embedding Ecosystems
Summary: Feature stores for ML pipelines expand from traditional tabular features toward embedding ecosystems. It pinpoints embedding-specific gaps—training data management, embedding quality assessment, and downstream monitoring—that standard feature stores don’t cover, and surveys candidate solutions. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Laurel Orr (Stanford University)
- 2. Atindriyo Sanyal (Uber)
- 3. Xiao Ling (Apple)
- 4. Karan Goel (Stanford University)
- 5. Megan Leszczynski (Stanford University)
BibTeX Citation
@article{orr_vldb21,
title = {{Managing ML Pipelines: Feature Stores and the Coming Wave of Embedding Ecosystems}},
author = {Orr, Laurel and Sanyal, Atindriyo and Ling, Xiao and Goel, Karan and Leszczynski, Megan},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {12},
pages = {3178--3181},
doi = {10.14778/3476311.3476402},
url = {https://doi.org/10.14778/3476311.3476402},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
Showing 6 of 6 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,442 | From BERT to GPT-3 Codex: Harnessing the Potential of Very Large Language Models for Data Management | 2022 | VLDB | 6.5988336e-05 |
| 5,773 | Optimizing Data Pipelines for Machine Learning in Feature Stores | 2023 | VLDB | 5.9987664e-05 |
| 6,662 | UPLIFT: Parallelization Strategies for Feature Transformations in Machine Learning Workloads | 2022 | VLDB | 5.7171651e-05 |
| 8,411 | SHiFT: An Efficient, Flexible Search Engine for Transfer Learning | 2023 | VLDB | 5.336291e-05 |
| 9,179 | FEBench: A Benchmark for Real-Time Relational Data Feature Extraction | 2023 | VLDB | 5.2121898e-05 |
| 10,897 | Tidehunter: Large-Value Storage With Minimal Data Relocation | 2026 | VLDB | 4.9793485e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 158 | Deep Learning for Entity Matching: A Design Space Exploration | 2018 | SIGMOD | 0.00028046388 |
| 205 | Snorkel: Rapid Training Data Creation with Weak Supervision | 2018 | VLDB | 0.00025181304 |
| 1,038 | ARDA: Automatic Relational Data Augmentation for Machine Learning | 2020 | VLDB | 0.00012370691 |
| 3,997 | Overton: A Data System for Monitoring and Improving Machine-Learned Products | 2020 | CIDR | 6.8655222e-05 |
| 9,766 | Bootleg: Chasing the Tail with Self-Supervised Named Entity Disambiguation | 2021 | CIDR | 5.1344318e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 7,613 | The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development | 2020 | SIGMOD |
| 2 | 11,821 | Towards Observability for Machine Learning Pipelines | 2022 | CIDR |
| 3 | 1,255 | Data Management in Machine Learning: Challenges, Techniques, and Systems | 2017 | SIGMOD |
| 4 | 2,187 | Production Machine Learning Pipelines: Empirical Analysis and Optimization Opportunities | 2021 | SIGMOD |
| 5 | 9,417 | Towards Observability for Production Machine Learning Pipelines | 2022 | VLDB |
| 6 | 11,426 | ML-Asset Management: Curation, Discovery, and Utilization | 2025 | VLDB |
| 7 | 1,153 | Data Management Challenges in Production Machine Learning | 2017 | SIGMOD |
| 8 | 5,773 | Optimizing Data Pipelines for Machine Learning in Feature Stores | 2023 | VLDB |
| 9 | 11,825 | Data Management Opportunities for Foundation Models | 2022 | CIDR |
| 10 | 7,544 | The Hopsworks Feature Store for Machine Learning | 2024 | SIGMOD |