List of ClearML AI Infrastructure Control Plane Customers
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Since 2010, our global team of researchers has been studying ClearML AI Infrastructure Control Plane 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 ClearML AI Infrastructure Control Plane for AI infrastructure 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 ClearML AI Infrastructure Control Plane for AI infrastructure include: Volkswagen Germany, a Germany based Automotive organisation with 293338 employees and revenues of $351.27 billion, Trax Retail, a Singapore based Professional Services organisation with 699 employees and revenues of $215.0 million, Meraki, a United Kingdom based Professional Services organisation with 10 employees and revenues of $3.5 million and many others.
Contact us if you need a completed and verified list of companies using ClearML AI Infrastructure Control Plane, 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 | Insight Source |
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Meraki | Professional Services | 10 | $3M | United Kingdom | ClearML | ClearML AI Infrastructure Control Plane | AI infrastructure | 2024 | n/a | In 2024 Meraki deployed ClearML AI Infrastructure Control Plane to manage experiment tracking, workflow automation, and hybrid on prem and cloud workloads for computer vision and LLM projects, reflecting a focused investment in AI infrastructure. The deployment centered on centralizing experiment metadata and pipeline orchestration to support parallel model development across small, distributed engineering teams. The implementation used ClearML Autoscalers and Pipelines as core functional modules, with ClearML AI Infrastructure Control Plane providing experiment tracking, scheduled and event-driven workflow automation, and autoscaling controls. Pipelines standardized CI/CD patterns for model training and evaluation, while Autoscalers dynamically provisioned cloud compute to match workload demand during training and inference work. Operational coverage targeted Meraki teams in the United States, where cloud compute autoscaling and pipeline standardization were applied to both computer vision and large language model projects. Integrations were limited to ClearML feature sets that orchestrate compute and CI/CD workflows, enabling centralized run management, artifact storage, and reproducible experiment lineage across distributed development activities. Governance emphasized standardized pipeline templates and automated workflow triggers to reduce manual coordination between data scientists and ops, with rollout organized around project-level onboarding of ClearML Pipelines and Autoscalers. Reported outcomes included improved collaboration and compute utilization, accelerating AI development cycles and reducing infrastructure overhead. | |
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Trax Retail | Professional Services | 699 | $215M | Singapore | ClearML | ClearML AI Infrastructure Control Plane | AI infrastructure | 2024 | n/a | In 2024, Trax Retail implemented ClearML AI Infrastructure Control Plane to consolidate model lifecycle artifacts and versioning within its computer vision stack. The implementation centered on a model repository built on ClearML ModelStore to manage model versions and converted formats such as TensorFlow Lite and CoreML, ensuring compatibility between detector, classifier and embedder components deployed on edge devices and cloud inference nodes. This deployment is categorized as AI infrastructure and addresses cross-environment model consistency for retail image analytics. Trax configured functional capabilities within ClearML AI Infrastructure Control Plane including artifact versioning, format conversion workflows, and model metadata management to track compatibility across component types. The model repository was structured to store converted artifacts, maintain lineage for model variants, and surface metadata needed for packaging models for edge runtime and cloud serving. Automation of conversion and tagging workflows reduced manual handling during packaging and release preparation. Integrations tied the ClearML ModelStore to ClearML AI Development Center and the ClearML AI Infrastructure Control Plane to provide centralized discovery, governance and controlled rollout pathways for models used across retail sites. Operational scope covered both edge and cloud deployment targets and impacted machine vision engineering and deployment operations teams responsible for detector, classifier and embedder maintenance. The case study reports that the approach enabled scalable, secure model management and reduced versioning complexity for production rollouts. | |
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Volkswagen Germany | Automotive | 293338 | $351.3B | Germany | ClearML | ClearML AI Infrastructure Control Plane | AI infrastructure | 2023 | n/a | In 2023 Volkswagen Germany's Machine Learning Research Lab deployed ClearML AI Infrastructure Control Plane as its AI infrastructure to centralize experiment orchestration and researcher access to compute. The deployment established a control plane for coordinating experiments, compute scheduling, and artifact management across the lab. ClearML AI Infrastructure Control Plane was configured for experiment management, workflow orchestration, dataset management, and model management to standardize reproducible workflows. The implementation emphasized orchestration capabilities, artifact lineage and unified access to heterogeneous compute resources to simplify researcher workflows and accelerate prototyping. Operational scope was concentrated in Volkswagen Germany's Machine Learning Research Lab, where teams used the ClearML control plane to provision runs and manage datasets and models. The lab deprecated its heterogeneous MLOps stack and reported higher cluster utilization and faster prototyping following the ClearML deployment. Governance and process changes included the introduction of role based access control and integrated orchestration to support compliance and operational efficiency, establishing standardized access controls and run governance for models and datasets. The ClearML AI Infrastructure Control Plane provided centralized experiment provenance and orchestration to improve operational consistency for research and prototyping activities. |
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