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Moog, an UKG AutoTime customer evaluated Workday Time and Attendance

Wayfair, a Korber HighJump WMS customer just evaluated Manhattan WMS

Cantor Fitzgerald, a Kyriba Treasury customer evaluated GTreasury

Swedbank, a Temenos T24 customer evaluated Oracle Flexcube

Westpac NZ, an Infosys Finacle customer evaluated nCino Bank OS

Michelin, an e2open customer evaluated Oracle Transportation Management

Citigroup, a VestmarkONE customer evaluated BlackRock Aladdin Wealth

Moog, an UKG AutoTime customer evaluated Workday Time and Attendance

Wayfair, a Korber HighJump WMS customer just evaluated Manhattan WMS

Cantor Fitzgerald, a Kyriba Treasury customer evaluated GTreasury

Swedbank, a Temenos T24 customer evaluated Oracle Flexcube

Westpac NZ, an Infosys Finacle customer evaluated nCino Bank OS

Michelin, an e2open customer evaluated Oracle Transportation Management

Citigroup, a VestmarkONE customer evaluated BlackRock Aladdin Wealth

List of ClearML AI Infrastructure Control Plane Customers

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Logo Customer Industry Empl. Revenue Country Vendor Application Category When SI Insight Insight Source
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.
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.
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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Buyer Intent: Companies Evaluating ClearML AI Infrastructure Control Plane

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