# The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox

arXiv: https://arxiv.org/abs/1409.3809 · HTML: https://mlsanity.com/paper/1409.3809/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2014.

## Built on by (most cited first, 8 of 8)

- [Management of Machine Learning Lifecycle Artifacts: A Survey](https://mlsanity.com/paper/2210.11831/) (2022)
- [SensiX++: Bringing MLOPs and Multi-tenant Model Serving to Sensory Edge Devices](https://mlsanity.com/paper/2109.03947/) (2021)
- [Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approach](https://mlsanity.com/paper/2410.21346/) (2024)
- [Accelerating Deep Learning Classification with Error-controlled Approximate-key Caching](https://mlsanity.com/paper/2112.06671/) (2021)
- [Cocktail: Leveraging Ensemble Learning for Optimized Model Serving in Public Cloud](https://mlsanity.com/paper/2106.05345/) (2021)
- [Synergy: Towards On-Body AI via Tiny AI Accelerator Collaboration on Wearables](https://mlsanity.com/paper/2401.08637/) (2023)
- [Combining Cloud and Mobile Computing for Machine Learning](https://mlsanity.com/paper/2402.04880/) (2024)
- [Fast Evaluation of DNN for Past Dataset in Incremental Learning](https://mlsanity.com/paper/2405.06296/) (2024)

## Abstract (arXiv)

To support complex data-intensive applications such as personalized recommendations, targeted advertising, and intelligent services, the data management community has focused heavily on the design of systems to support training complex models on large datasets. Unfortunately, the design of these systems largely ignores a critical component of the overall analytics process: the deployment and serving of models at scale. In this work, we present Velox, a new component of the Berkeley Data Analytics Stack. Velox is a data management system for facilitating the next steps in real-world, large-scale analytics pipelines: online model management, maintenance, and serving. Velox provides end-user applications and services with a low-latency, intuitive interface to models, transforming the raw statistical models currently trained using existing offline large-scale compute frameworks into full-blown, end-to-end data products capable of recommending products, targeting advertisements, and personalizing web content. To provide up-to-date results for these complex models, Velox also facilitates lightweight online model maintenance and selection (i.e., dynamic weighting). In this paper, we describe the challenges and architectural considerations required to achieve this functionality, including the abilities to span online and offline systems, to adaptively adjust model materialization strategies, and to exploit inherent statistical properties such as model error tolerance, all while operating at "Big Data" scale.