List of DVC.ai Customers
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Since 2010, our global team of researchers has been studying DVC.ai customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased DVC.ai for MLOps Platforms from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using DVC.ai for MLOps Platforms include: UBS, a Switzerland based Banking and Financial Services organisation with 106789 employees and revenues of $57.05 billion, Celus, a Germany based Professional Services organisation with 80 employees and revenues of $10.0 million, Degould, a United Kingdom based Automotive organisation with 60 employees and revenues of $5.0 million and many others.
Contact us if you need a completed and verified list of companies using DVC.ai, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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Celus | Professional Services | 80 | $10M | Germany | iterative | DVC.ai | MLOps Platforms | 2024 | n/a |
In 2024, Celus implemented DVC.ai within its MLOps Platforms tooling to support dataset management and reproducible machine learning workflows for its AI driven electronics design efforts in Germany. Public job postings for the Munich engineering organization explicitly list hands on experience with DVC, indicating practical use of DVC.ai alongside the open source DVC tooling for dataset version control and workflow reproducibility.
The implementation emphasizes core MLOps Platforms capabilities including dataset versioning, pipeline reproducibility, experiment metadata capture, and model artifact referencing, aligning with typical DVC.ai functionality. Configurations are repository based with dataset references and pipeline definitions stored alongside code, enabling traceable data lineage and repeatable training runs across development and engineering workflows.
Operational scope centers on Celus engineering and data science teams in Munich and across Germany, with recruiting and hiring criteria adjusted to require DVC competency to accelerate tool adoption. Governance and process changes focus on code first dataset management and reproducible pipeline practices, with adoption driven through recruitment and embedded engineering workflows rather than specified procurement outcomes.
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Degould | Automotive | 60 | $5M | United Kingdom | iterative | DVC.ai | MLOps Platforms | 2021 | n/a |
In 2021 DeGould implemented DVC.ai in the MLOps Platforms category to improve reproducibility and collaboration for machine learning models that power automated vehicle inspection and defect detection pipelines in the United Kingdom. The implementation targeted operationalizing experiment sharing and artifact versioning to support iterative model development across the companys ML engineering and data science teams.
Deployment centered on Iterative's DVC for data and model versioning and CML together with DVC Studio for experiment tracking and collaborative review, reflecting vendor statements that identify DeGould as a user. Configuration included tracked training runs, dataset snapshots, and versioned model artifacts stored alongside code in repository centric workflows to preserve reproducibility and auditability of experiments.
The implementation connected model versioning to CI driven experiment execution and shared run artifacts, enabling teams to push reproducible changes into validation cycles without ad hoc file sharing. Operational coverage focused on DeGoulds UK-based teams developing automated vehicle inspection and defect detection pipelines, aligning the MLOps Platforms functionality to the companys core business function of automated inspection.
Governance emphasized commit level traceability, standardized experiment naming and shared DVC remotes for artifact governance, and rollout practices that promoted experiment sharing across contributors. DVC.ai delivered MLOps Platforms capabilities for centralized data and model version control and structured collaboration workflows, which Iterative reported supported faster experiment sharing and higher team productivity.
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UBS | Banking and Financial Services | 106789 | $57.1B | Switzerland | iterative | DVC.ai | MLOps Platforms | 2024 | n/a |
In 2024, UBS implemented DVC.ai as part of its MLOps Platforms strategy to support ML model and data versioning within its financial services operations in Switzerland. Iterative has publicly listed UBS among enterprise customers for its commercial DVC offerings, indicating UBS is using DVC-related enterprise SaaS to govern model and data artifacts across development workflows.
The deployment centers on canonical MLOps capabilities, with emphasis on persistent model versioning, reproducible data versioning, experiment tracking, artifact management, and lineage capture. Product-level adoption is inferred to include Iterative modules such as DVC Studio and DataChain based on Iterative product announcements and customer-count statements, aligning with common MLOps Patterns for traceability and collaborative model development.
Operational integrations are described at a platform level, connecting DVC.ai to code repositories, enterprise object stores, and CI CD pipelines typical of MLOps Platforms, while exposing model and data artifacts for review by data science and model validation teams. The implementation scope spans data science, model risk and governance functions, supporting cross functional workflows for model lifecycle orchestration and reproducibility within UBS.
Governance and process changes focus on standardized version control for datasets and models, stronger audit trails for model provenance, and formalized handoffs between development and validation teams. UBS use of DVC.ai positions the organization to centralize artifact governance and support regulated model management processes common in banking and financial services.
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