# Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML

arXiv: https://arxiv.org/abs/1909.00084 · HTML: https://mlsanity.com/paper/1909.00084/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2019.

## Built on by (most cited first, 6 of 6)

- [Data Debugging with Shapley Importance over End-to-End Machine Learning Pipelines](https://mlsanity.com/paper/2204.11131/) (2022)
- [End-to-end Optimization of Machine Learning Prediction Queries](https://mlsanity.com/paper/2206.00136/) (2022)
- [Data Cleaning and Machine Learning: A Systematic Literature Review](https://mlsanity.com/paper/2310.01765/) (2023)
- [Share the Tensor Tea: How Databases can Leverage the Machine Learning Ecosystem](https://mlsanity.com/paper/2209.04579/) (2022)
- [Mining Robust Default Configurations for Resource-constrained AutoML](https://mlsanity.com/paper/2202.09927/) (2022)
- [An efficient and flexible inference system for serving heterogeneous ensembles of deep neural networks](https://mlsanity.com/paper/2208.14049/) (2022)

## Abstract (arXiv)

Machine learning (ML) has proven itself in high-value web applications such as search ranking and is emerging as a powerful tool in a much broader range of enterprise scenarios including voice recognition and conversational understanding for customer support, autotuning for videoconferencing, intelligent feedback loops in large-scale sysops, manufacturing and autonomous vehicle management, complex financial predictions, just to name a few. Meanwhile, as the value of data is increasingly recognized and monetized, concerns about securing valuable data and risks to individual privacy have been growing. Consequently, rigorous data management has emerged as a key requirement in enterprise settings. How will these trends (ML growing popularity, and stricter data governance) intersect? What are the unmet requirements for applying ML in enterprise settings? What are the technical challenges for the DB community to solve? In this paper, we present our vision of how ML and database systems are likely to come together, and early steps we take towards making this vision a reality.