Machine Learning for Cloud Data Systems: the Progress so far and the Path Forward

Summary: Tutorial surveying ML’s real-world adoption in cloud data systems versus its promise, then outlining enterprise hurdles: explainability, debugging, deployment and management, data constraints/anonymization, and accumulated model technical debt. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID: hfee57080101766b3
Venue: VLDB
Year: 2021
Pagerank: 5.4500623e-05
Overall Rank: 7,806 | 47.52%
DOI: 10.14778/3476311.3476408

Incoming Non-self Citations Over Time

Authors

1. Alekh Jindal (Microsoft)
2. Matteo Interlandi (Microsoft)

BibTeX Citation

@article{jindal_vldb21,
        title = {{Machine Learning for Cloud Data Systems: the Progress so far and the Path Forward}},
        author = {Jindal, Alekh and Interlandi, Matteo},
        journal = {PVLDB},
        series = {{VLDB} '21},
        volume = {14},
        number = {12},
        pages = {3202--3205},
        doi = {10.14778/3476311.3476408},
        url = {https://doi.org/10.14778/3476311.3476408},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
3,949	Deploying a Steered Query Optimizer in Production at Microsoft	2022	SIGMOD	6.9052796e-05
4,442	From BERT to GPT-3 Codex: Harnessing the Potential of Very Large Language Models for Data Management	2022	VLDB	6.5988336e-05
11,915	Machine Programming: Turning Data into Programmer Productivity	2022	VLDB	4.9793485e-05

Outgoing Citations (Sorted by Pagerank)

Showing 22 of 22 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
40	The Case for Learned Index Structures	2018	SIGMOD	0.00046284649
145	Neo: A Learned Query Optimizer	2019	VLDB	0.0002908188
682	Robust Estimation of Resource Consumption for SQL Queries using Statistical Techniques	2012	VLDB	0.0001481781
692	Selectivity Estimation for Range Predicates using Lightweight Models	2019	VLDB	0.00014741011
1,064	Are We Ready For Learned Cardinality Estimation?	2021	VLDB	0.00012202282
1,233	Dhalion: Self-Regulating Stream Processing in Heron	2017	VLDB	0.00011405873
1,279	AI Meets AI: Leveraging Query Executions to Improve Index Recommendations	2019	SIGMOD	0.00011226878
1,433	Towards a Learning Optimizer for Shared Clouds	2019	VLDB	0.00010677711
1,734	Flow-Loss: Learning Cardinality Estimates That Matter	2021	VLDB	9.7545773e-05
2,834	Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings	2020	SIGMOD	7.9560627e-05
3,465	DIAMetrics: Benchmarking Query Engines at Scale	2020	VLDB	7.2808932e-05
3,545	Computation Reuse in Analytics Job Service at Microsoft	2018	SIGMOD	7.2134803e-05
3,937	Understanding and Benchmarking the Impact of GDPR on Database Systems	2020	VLDB	6.9158079e-05
4,037	HedgeCut: Maintaining Randomised Trees for Low-Latency Machine Unlearning	2021	SIGMOD	6.8390354e-05
5,110	Steering Query Optimizers: A Practical Take on Big Data Workloads	2021	SIGMOD	6.269351e-05
6,901	Seagull: An Infrastructure for Load Prediction and Optimized Resource Allocation	2021	VLDB	5.6520335e-05
7,084	KEA: Tuning an Exabyte-Scale Data Infrastructure	2021	SIGMOD	5.6029455e-05
7,363	PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost!	2021	VLDB	5.5418564e-05
7,759	AutoToken: Predicting Peak Parallelism for Big Data Analytics at Microsoft	2020	VLDB	5.4575614e-05
8,404	Spur: Mitigating Slow Instances in Large-Scale Streaming Pipelines	2020	SIGMOD	5.3385814e-05
9,358	Phoebe: A Learning-based Checkpoint Optimizer	2021	VLDB	5.1869771e-05
9,993	SparkCruise: Handsfree Computation Reuse in Spark	2019	VLDB	5.0988164e-05

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