Data Virtualization for Machine Learning

• URL: http://arxiv.org/abs/2507.17293v1
• Date: Wed, 23 Jul 2025 07:53:56 GMT
• Title: Data Virtualization for Machine Learning
• Authors: Saiful Khan, Joyraj Chakraborty, Philip Beaucamp, Niraj Bhujel, Min Chen,
• Abstract summary: Machine learning (ML) teams have multiple concurrent ML for different applications.<n> Organizationally, there is a large amount of intermediate data to be stored, processed, and maintained.<n>Data virtualization becomes a critical technology in an infrastructure to serve ML.
• Score: 2.3137533618070503
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Nowadays, machine learning (ML) teams have multiple concurrent ML workflows for different applications. Each workflow typically involves many experiments, iterations, and collaborative activities and commonly takes months and sometimes years from initial data wrangling to model deployment. Organizationally, there is a large amount of intermediate data to be stored, processed, and maintained. \emph{Data virtualization} becomes a critical technology in an infrastructure to serve ML workflows. In this paper, we present the design and implementation of a data virtualization service, focusing on its service architecture and service operations. The infrastructure currently supports six ML applications, each with more than one ML workflow. The data virtualization service allows the number of applications and workflows to grow in the coming years.

Related papers

• On Domain-Adaptive Post-Training for Multimodal Large Language Models [72.67107077850939]
  This paper systematically investigates domain adaptation of MLLMs via post-training.<n>We focus on data synthesis, training pipeline, and task evaluation.<n>We conduct experiments in high-impact domains such as biomedicine, food, and remote sensing.
  arXiv  Detail & Related papers  (2024-11-29T18:42:28Z)
• WorkflowLLM: Enhancing Workflow Orchestration Capability of Large Language Models [105.46456444315693]
  We presentLLM, a data-centric framework to enhance the capability of large language models in workflow orchestration. It first constructs a large-scale fine-tuningBench with 106,763 samples, covering 1,503 APIs from 83 applications across 28 categories. LlamaLlama demonstrates a strong capacity to orchestrate complex APIs, while also achieving notable generalization performance.
  arXiv  Detail & Related papers  (2024-11-08T09:58:02Z)
• Spider2-V: How Far Are Multimodal Agents From Automating Data Science and Engineering Workflows? [73.81908518992161]
  We introduce Spider2-V, the first multimodal agent benchmark focusing on professional data science and engineering. Spider2-V features real-world tasks in authentic computer environments and incorporating 20 enterprise-level professional applications. These tasks evaluate the ability of a multimodal agent to perform data-related tasks by writing code and managing the GUI in enterprise data software systems.
  arXiv  Detail & Related papers  (2024-07-15T17:54:37Z)
• The Synergy between Data and Multi-Modal Large Language Models: A Survey from Co-Development Perspective [53.48484062444108]
  We find that the development of models and data is not two separate paths but rather interconnected. On the one hand, vaster and higher-quality data contribute to better performance of MLLMs; on the other hand, MLLMs can facilitate the development of data. To promote the data-model co-development for MLLM community, we systematically review existing works related to MLLMs from the data-model co-development perspective.
  arXiv  Detail & Related papers  (2024-07-11T15:08:11Z)
• Couler: Unified Machine Learning Workflow Optimization in Cloud [6.769259207650922]
  Couler is a system designed for unified ML workflow optimization in the cloud. We integrate Large Language Models (LLMs) into workflow generation, and provide a unified programming interface for various workflow engines. Couer has successfully improved the CPU/Memory utilization by more than 15% and the workflow completion rate by around 17%.
  arXiv  Detail & Related papers  (2024-03-12T12:47:32Z)
• In Situ Framework for Coupling Simulation and Machine Learning with Application to CFD [51.04126395480625]
  Recent years have seen many successful applications of machine learning (ML) to facilitate fluid dynamic computations. As simulations grow, generating new training datasets for traditional offline learning creates I/O and storage bottlenecks. This work offers a solution by simplifying this coupling and enabling in situ training and inference on heterogeneous clusters.
  arXiv  Detail & Related papers  (2023-06-22T14:07:54Z)
• MLOps: A Step Forward to Enterprise Machine Learning [0.0]
  This research presents a detailed review of MLOps, its benefits, difficulties, evolutions, and important underlying technologies. The MLOps workflow is explained in detail along with the various tools necessary for both model and data exploration and deployment. This article also puts light on the end-to-end production of ML projects using various maturity levels of automated pipelines.
  arXiv  Detail & Related papers  (2023-05-27T20:44:14Z)
• Efficient Device Scheduling with Multi-Job Federated Learning [64.21733164243781]
  We propose a novel multi-job Federated Learning framework to enable the parallel training process of multiple jobs. We propose a reinforcement learning-based method and a Bayesian optimization-based method to schedule devices for multiple jobs while minimizing the cost. Our proposed approaches significantly outperform baseline approaches in terms of training time (up to 8.67 times faster) and accuracy (up to 44.6% higher)
  arXiv  Detail & Related papers  (2021-12-11T08:05:11Z)
• Exploring the potential of flow-based programming for machine learning deployment in comparison with service-oriented architectures [8.677012233188968]
  We argue that part of the reason is infrastructure that was not designed for activities around data collection and analysis. We propose to consider flow-based programming with data streams as an alternative to commonly used service-oriented architectures for building software applications.
  arXiv  Detail & Related papers  (2021-08-09T15:06:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.