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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.
783
aziende
Abbiamo dati su 783 aziende che usano DVC. La nostra lista di clienti DVC è disponibile per il download ed è arricchita con specifiche vitali dell'azienda, incluse classificazione industriale, dimensioni organizzative, posizione geografica, round di finanziamenti e cifre di ricavi, tra gli altri.
Azienda | Paese | Settore | Dipendenti | Entrate |
---|---|---|---|---|
D'Andrea Visual Communications | Stati Uniti | Printing Services | 51 | $5.9M |
Contra Costa Community College District | Stati Uniti | Higher Education | 1K | |
Schneider Electric | Francia | Automation Machinery Manufacturing | 166K | $26B |
Schools (Government) | Australia | Government Administration | 16K | |
![]() BenchSci | Canada | Software Development | 431 | $6.1M |
Université de Montpellier | Francia | Education | 1K | |
Disney | Stati Uniti | Broadcast Media Production And Distribution | 10K | |
Disney Parks, Experiences and Products | Francia | Entertainment Providers | 10K | |
Swinburne University of Technology | Australia | Higher Education | 6.2K | |
University of British Columbia | Canada | Education | 22K | $2.9B |
Dichterbij | Paesi Bassi | Hospitals And Health Care | 5K | $799M |
Macquarie University | Australia | Education | 3K | $858M |
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DVC è utilizzata in 35 paesi
Ci sono 5 alternative a DVC
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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.
Version control systems play a crucial role in enabling companies to efficiently manage and track changes in their codebase. One popular tool in this category is DVC, or Data Version Control. Companies utilize DVC for various reasons, leveraging its unique features and advantages to streamline their development processes and ensure data integrity.
DVC offers a seamless way to version data along with code, providing a comprehensive snapshot of the entire ML pipeline. This feature sets DVC apart from traditional version control systems, which often focus solely on code changes.
With DVC, team members can collaborate effectively by sharing reproducible and versioned data and models. Unlike other tools, DVC simplifies the process of tracking, sharing, and reproducing data-driven experiments.
DVC is designed to handle large datasets efficiently, enabling companies to scale their data projects without sacrificing performance or data integrity. This scalability sets DVC apart from other version control systems that may struggle with handling big data.
One of the key benefits of DVC is its ability to track the lineage of data, making it easier to trace the source of data used in models. This feature enhances transparency and reproducibility, setting DVC apart from traditional version control systems.
DVC seamlessly integrates with popular ML frameworks and tools, enhancing its usability and reducing friction in existing workflows. This integration makes DVC a preferred choice over other tools that may require complex configurations for compatibility.
By leveraging the benefits of DVC, companies can enhance their data versioning processes, improve collaboration among teams, and ensure scalability and performance in managing their machine learning projects.
Some notable companies that leverage DVC (Data Version Control) for managing their data science projects include Airbnb, Spotify, and Pachyderm. These companies utilize DVC to streamline their machine learning workflows, improve collaboration among data teams, and ensure reproducibility in their models.
Airbnb: Airbnb adopted DVC to enhance the reproducibility of their machine learning models. By using DVC, Airbnb's data science team can track changes to their datasets and models effectively, helping them reproduce results and collaborate efficiently on various projects. They started using DVC in 2018 and have since seen significant improvements in their data workflows.
Spotify: Spotify implements DVC in managing their vast amounts of data for targeted music recommendations and playlist personalization. DVC enables Spotify's data engineers to version control their datasets and models, ensuring that any changes made are well-documented and reproducible. Spotify integrated DVC into their data pipelines in 2019, leading to more streamlined data processes and improved model performance.
Pachyderm: Pachyderm, a company specializing in data versioning and automation, naturally uses DVC to drive their data infrastructure. They rely on DVC to manage the versioning of large-scale datasets and machine learning models within their platform. By leveraging DVC, Pachyderm ensures that their users have complete visibility and control over data lineage and model iterations, enhancing the overall data management experience. Pachyderm integrated DVC into their system from the early stages of development, showcasing its importance in their data-driven approach.
These case studies highlight how leading companies in the tech industry, like Airbnb, Spotify, and Pachyderm, benefit from using DVC as a powerful tool for enhancing their data science projects and ensuring robust version control practices.
Puoi accedere a un elenco aggiornato di aziende che utilizzano DVC visitando TheirStack.com. La nostra piattaforma fornisce un database completo di aziende che utilizzano varie tecnologie e strumenti interni.
Fino ad ora, abbiamo dati su 783 aziende che utilizzano DVC.
DVC è utilizzato da una vasta gamma di organizzazioni in vari settori, inclusi "Printing Services", "Higher Education", "Automation Machinery Manufacturing", "Government Administration", "Software Development", "Education", "Broadcast Media Production And Distribution", "Entertainment Providers", "Higher Education", "Education". Per un elenco completo di tutti i settori che utilizzano DVC, si prega di visitare TheirStack.com.
Alcune delle aziende che utilizzano DVC includono D'Andrea Visual Communications, Contra Costa Community College District, Schneider Electric, Schools (Government), BenchSci, Université de Montpellier, Disney, Disney Parks, Experiences and Products, Swinburne University of Technology, University of British Columbia e molte altre. Puoi trovare un elenco completo di 783 aziende che utilizzano DVC su TheirStack.com.
Secondo i nostri dati, DVC è più popolare in Stati Uniti (205 companies), Regno Unito (68 companies), Francia (38 companies), Germania (35 companies), Australia (32 companies), Canada (24 companies), India (14 companies), Paesi Bassi (13 companies), Spagna (13 companies), Sudafrica (10 companies). Tuttavia, è utilizzato da aziende in tutto il mondo.
Puoi trovare aziende che utilizzano DVC cercandolo su TheirStack.com. Tracciamo le offerte di lavoro di milioni di aziende e le utilizziamo per scoprire quali tecnologie e strumenti interni stanno utilizzando.