# NeurDB: An AI-powered Autonomous Data System

arXiv: https://arxiv.org/abs/2405.03924 · HTML: https://mlsanity.com/paper/2405.03924/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 76 references that are in the map.
- First posted on arXiv 7 May 2024.

## Builds on (most cited first, 30 of 33)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://mlsanity.com/paper/2106.01345/) (2021)
- [Learning Transferable Architectures for Scalable Image Recognition](https://mlsanity.com/paper/1707.07012/) (2017)
- [Pruning neural networks without any data by iteratively conserving synaptic flow](https://mlsanity.com/paper/2006.05467/) (2020)
- [Neural Architecture Search without Training](https://mlsanity.com/paper/2006.04647/) (2020)
- [Zero-Cost Proxies for Lightweight NAS](https://mlsanity.com/paper/2101.08134/) (2021)
- [Clipper: A Low-Latency Online Prediction Serving System](https://mlsanity.com/paper/1612.03079/) (2016)
- [Hierarchical Representations for Efficient Architecture Search](https://mlsanity.com/paper/1711.00436/) (2017)
- [Neural Factorization Machines for Sparse Predictive Analytics](https://mlsanity.com/paper/1708.05027/) (2017)
- [How Powerful are Performance Predictors in Neural Architecture Search?](https://mlsanity.com/paper/2104.01177/) (2021)
- [Privacy Preserving Vertical Federated Learning for Tree-based Models](https://mlsanity.com/paper/2008.06170/) (2020)
- [Neo: A Learned Query Optimizer](https://mlsanity.com/paper/1904.03711/) (2019)
- [Bao: Learning to Steer Query Optimizers](https://mlsanity.com/paper/2004.03814/) (2020)
- [Rafiki: Machine Learning as an Analytics Service System](https://mlsanity.com/paper/1804.06087/) (2018)
- [Hyper-Decision Transformer for Efficient Online Policy Adaptation](https://mlsanity.com/paper/2304.08487/) (2023)
- [Database Meets Deep Learning: Challenges and Opportunities](https://mlsanity.com/paper/1906.08986/) (2019)
- [Lero: A Learning-to-Rank Query Optimizer](https://mlsanity.com/paper/2302.06873/) (2023)
- [Large Language Models for Business Process Management: Opportunities and Challenges](https://mlsanity.com/paper/2304.04309/) (2023)
- [Balsa: Learning a Query Optimizer Without Expert Demonstrations](https://mlsanity.com/paper/2201.01441/) (2022)
- [Unifying and Boosting Gradient-Based Training-Free Neural Architecture Search](https://mlsanity.com/paper/2201.09785/) (2022)
- [Towards Dynamic and Safe Configuration Tuning for Cloud Databases](https://mlsanity.com/paper/2203.14473/) (2022)
- [End-to-end Optimization of Machine Learning Prediction Queries](https://mlsanity.com/paper/2206.00136/) (2022)
- [MLCask: Efficient Management of Component Evolution in Collaborative Data Analytics Pipelines](https://mlsanity.com/paper/2010.10246/) (2020)
- [TRACER: A Framework for Facilitating Accurate and Interpretable Analytics for High Stakes Applications](https://mlsanity.com/paper/2003.12012/) (2020)
- [Learning Models over Relational Data using Sparse Tensors and Functional Dependencies](https://mlsanity.com/paper/1703.04780/) (2017)
- [Model Slicing for Supporting Complex Analytics with Elastic Inference Cost and Resource Constraints](https://mlsanity.com/paper/1904.01831/) (2019)
- [Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofs](https://mlsanity.com/paper/2311.15310/) (2023)
- [Powering In-Database Dynamic Model Slicing for Structured Data Analytics](https://mlsanity.com/paper/2405.00568/) (2024)
- [Polyjuice: High-Performance Transactions via Learned Concurrency Control](https://mlsanity.com/paper/2105.10329/) (2021)
- [Kepler: Robust Learning for Faster Parametric Query Optimization](https://mlsanity.com/paper/2306.06798/) (2023)

## Built on by (most cited first, 5 of 5)

- [MEMO: Fine-grained Tensor Management For Ultra-long Context LLM Training](https://mlsanity.com/paper/2407.12117/) (2024)
- [In-Context Adaptation to Concept Drift for Learned Database Operations](https://mlsanity.com/paper/2505.04404/) (2025)
- [Generative AI for Healthcare: Fundamentals, Challenges, and Perspectives](https://mlsanity.com/paper/2510.24551/) (2025)
- [NeurStore: Efficient In-database Deep Learning Model Management System](https://mlsanity.com/paper/2509.03228/) (2025)
- [Beyond Model Base Retrieval: Weaving Knowledge to Master Fine-grained Neural Network Design](https://mlsanity.com/paper/2507.15336/) (2025)

## Abstract (arXiv)

In the wake of rapid advancements in artificial intelligence (AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB (AIxDB) promises a new generation of data systems, which will relieve the burden on end-users across all industry sectors by featuring AI-enhanced functionalities, such as personalized and automated in-database AI-powered analytics, self-driving capabilities for improved system performance, etc. In this paper, we explore the evolution of data systems with a focus on deepening the fusion of AI and DB. We present NeurDB, an AI-powered autonomous data system designed to fully embrace AI design in each major system component and provide in-database AI-powered analytics. We outline the conceptual and architectural overview of NeurDB, discuss its design choices and key components, and report its current development and future plan.