技术使用统计数据和市场份额

如何针对DVC用户进行定位

如何自定义此列表？: 您可以通过筛选地理位置、行业、公司规模、收入、技术使用情况、职位等来根据您的需求定制这些数据。您可以以Excel或CSV格式下载数据。
如何在公司采用此技术时收到提醒？: 您可以获得有关此数据的提醒。您可以通过选择您感兴趣的技术来开始，然后当有新公司使用该技术时，您将会在您的收件箱中收到提醒。
如何将这些数据导入我的CRM？: 您可以将这些数据导出到一个Excel文件，然后导入到您的CRM中。您也可以将这些数据导出到API。

使用 DVC 的公司

Q: 什么是DVC?

DVC short for Data Version Control is a technology that falls under the broader category of Version Control Systems DVC is specifically designed to help data scientists and machine learning engineers manage and version their data enabling reproducibility and collaboration in data science projects Unlike traditional version control systems that focus on code DVC allows users to track changes to datasets models and experiments making it a crucial tool in the field of data science Founded in 2017 by Dmitry Petrov DVC stemmed from the increasing need for better data management in the rapidly evolving field of machine learning Petrov recognized the challenges faced by data scientists in tracking and versioning large datasets leading to the inception of DVC as a solution to address these pain points The motivation behind DVC was to provide a lightweight yet powerful tool for data versioning that seamlessly integrates with existing workflows in data science projects Currently DVC holds a niche market share within the realm of data version control tools As the importance of reproducibility and collaboration in data science continues to grow the demand for robust data versioning solutions like DVC is expected to rise With the increasing adoption of machine learning in various industries it is forecasted that DVC will experience continued growth in market share as organizations prioritize efficient data management practices to ensure the integrity and reliability of their machine learning models

Q: 哪些国家使用DVC最多？

根据我们的数据，DVC 在 美国 (237 companies), 英国 (83 companies), 法国 (58 companies), 德国 (41 companies), 澳大利亚 (34 companies), 加拿大 (22 companies), 荷兰 (17 companies), 印度 (16 companies), 西班牙 (15 companies), 瑞士 (14 companies) 最受欢迎。然而，它被全世界的公司所使用。

It is an open-source Version Control System for data science and machine learning projects. It is designed to handle large files, data sets, machine learning models, and metrics as well as code.

Configuration Management dvc.org

1,017

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技术使用统计数据和市场份额

如何针对DVC用户进行定位

如何自定义此列表？: 您可以通过筛选地理位置、行业、公司规模、收入、技术使用情况、职位等来根据您的需求定制这些数据。您可以以Excel或CSV格式下载数据。
如何在公司采用此技术时收到提醒？: 您可以获得有关此数据的提醒。您可以通过选择您感兴趣的技术来开始，然后当有新公司使用该技术时，您将会在您的收件箱中收到提醒。
如何将这些数据导入我的CRM？: 您可以将这些数据导出到一个Excel文件，然后导入到您的CRM中。您也可以将这些数据导出到API。

常见问题

我们的数据来自于从数百万家公司收集的招聘信息。我们在公司网站、招聘平台和其他招聘平台上监控这些招聘信息。分析招聘信息提供了一种可靠的方法来了解公司正在使用的技术，包括他们使用的内部工具。

我们每天更新数据，以确保您访问的是最新的可用信息。这一频繁的更新过程保证了我们的洞察力和情报反映了行业内的最新发展和趋势。

DVC, short for Data Version Control, is a technology that falls under the broader category of Version Control Systems. DVC is specifically designed to help data scientists and machine learning engineers manage and version their data, enabling reproducibility and collaboration in data science projects. Unlike traditional version control systems that focus on code, DVC allows users to track changes to datasets, models, and experiments, making it a crucial tool in the field of data science.

Founded in 2017 by Dmitry Petrov, DVC stemmed from the increasing need for better data management in the rapidly evolving field of machine learning. Petrov recognized the challenges faced by data scientists in tracking and versioning large datasets, leading to the inception of DVC as a solution to address these pain points. The motivation behind DVC was to provide a lightweight, yet powerful tool for data versioning that seamlessly integrates with existing workflows in data science projects.

Currently, DVC holds a niche market share within the realm of data version control tools. As the importance of reproducibility and collaboration in data science continues to grow, the demand for robust data versioning solutions like DVC is expected to rise. With the increasing adoption of machine learning in various industries, it is forecasted that DVC will experience continued growth in market share as organizations prioritize efficient data management practices to ensure the integrity and reliability of their machine learning models.

您可以访问 TheirStack.com，获取使用 DVC 的公司更新名单。我们的平台提供了一个全面的数据库，涵盖了使用各种技术和内部工具的公司。

截至目前，我们拥有关于 1,017 家使用 DVC 的公司的数据。

DVC 被广泛应用于包括 "Entertainment Providers", "Higher Education", "Printing Services", "Hospitals And Health Care", "E-Learning Providers", "Software Development", "Higher Education", "Manufacturing", "Higher Education" 在内的各个行业的各种组织中。欲了解所有使用 DVC 的行业的完整列表，请访问 TheirStack.com。

一些使用DVC的公司包括Disney Parks, Experiences and Products, Contra Costa Community College District, D'Andrea Visual Communications, Dichterbij, Université de Montpellier, BenchSci, RMIT University, Dokkumer Vlaggen Centrale, Macquarie University, Gemeenschappelijke Gemeenschapscommissie van Brussel-Hoofdstad以及更多公司。您可以在TheirStack.com上找到使用DVC的1,017家公司完整列表。

根据我们的数据，DVC 在美国 (237 companies), 英国 (83 companies), 法国 (58 companies), 德国 (41 companies), 澳大利亚 (34 companies), 加拿大 (22 companies), 荷兰 (17 companies), 印度 (16 companies), 西班牙 (15 companies), 瑞士 (14 companies) 最受欢迎。然而，它被全世界的公司所使用。

您可以在TheirStack.com上搜索DVC，来找到使用该技术的公司。我们跟踪数百万家公司的招聘信息，并借此发现他们正在使用的技术和内部工具。