List of Google Cloud AI Hub Customers
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Since 2010, our global team of researchers has been studying Google Cloud AI Hub 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 Google Cloud AI Hub for ML and Data Science 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 Google Cloud AI Hub for ML and Data Science Platforms include: ASML, a Netherlands based Manufacturing organisation with 44027 employees and revenues of $35.89 billion, Fusionex, a Malaysia based Professional Services organisation with 600 employees and revenues of $25.0 million, Descartes Labs, a United States based Professional Services organisation with 11 employees and revenues of $2.0 million and many others.
Contact us if you need a completed and verified list of companies using Google Cloud AI Hub, 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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ASML | Manufacturing | 44027 | $35.9B | Netherlands | Google Cloud AI Hub | ML and Data Science Platforms | 2018 | Rackspace |
In 2018, ASML implemented Google Cloud AI Hub as part of a broader Google Cloud AI and machine learning adoption to accelerate engineering and model training, a program tied to manufacturing engineering operations in the Netherlands. The deployment is framed within the ML and Data Science Platforms category and centered on development workflows for engineering teams to shorten product release cycles and reduce daily engineer effort.
The implementation used Google Cloud AI Platform Notebooks for interactive model development, Kubeflow for pipeline orchestration and repeatable training, and Google Cloud AI Hub for model and artifact sharing and collaboration. Google Cloud AI Hub served as the catalog and collaboration layer, enabling reuse of notebooks and models across engineering groups and supporting standard ML lifecycle activities such as experimentation, packaging, and model publishing.
Rackspace participated as the system integrator to support cloud deployment and operational onboarding within Google Cloud, aligning notebook instances, pipeline execution, and artifact storage. The technical architecture emphasized cloud native tooling on Google Cloud, linking AI Platform Notebooks, Kubeflow pipelines, and AI Hub into a cohesive ML environment used by ASML engineering teams across manufacturing sites in the Netherlands.
Governance and rollout included hands on workshops delivered by Kubeflow and AI Hub teams at ASML to train engineers and establish shared practices for model cataloging and notebook lifecycle management. The program restructured developer workflows toward collaborative model reuse and standardized pipeline execution, supporting faster iteration in engineering model training.
ASML reported outcomes including shortened product release cycles, improved time to market, improved data query performance, and engineers saving hours per day as a result of the Google Cloud AI and machine learning adoption and the use of Google Cloud AI Hub.
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Descartes Labs | Professional Services | 11 | $2M | United States | Google Cloud AI Hub | ML and Data Science Platforms | 2019 | n/a |
In 2019, Descartes Labs implemented Google Cloud AI Hub as an internal repository. Descartes Labs uses Google Cloud AI Hub within the ML and Data Science Platforms category to support geospatial modelling and supply chain analytics.
Google Cloud AI Hub is used to collect and catalog ML models, Jupyter notebooks, and Kubeflow pipelines, providing metadata driven discoverability and reusable artifact packaging. The deployment emphasizes artifact management and access control, using granular permissions and cataloging to enable sharing across teams and to surface models and pipelines for analytics workflows.
The implementation explicitly targets governance and improved discoverability of ML assets in the United States, positioning Google Cloud AI Hub as the company wide ML artifact repository. The usage is documented in the Google Cloud AI Hub blog, which describes Descartes Labs leveraging AI Hub for discovery, granular permissions, and sharing across teams, aligning the Company Google Cloud AI Hub ML and Data Science Platforms relationship with its geospatial modelling and supply chain analytics business functions.
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Fusionex | Professional Services | 600 | $25M | Malaysia | Google Cloud AI Hub | ML and Data Science Platforms | 2020 | n/a |
In 2020, Fusionex explored Google Cloud AI Hub as part of its ML and Data Science Platforms adoption, aligning the product with its existing Google Cloud AI tooling. Fusionex incorporated Google AI tooling into its analytics platform and the Google Cloud customer case study indicates AI Hub was part of the next phase they were exploring, so Google Cloud AI Hub usage is recorded as exploratory in this account.
The exploration focused on capabilities common to ML and Data Science Platforms, specifically enabling model and pipeline reuse, a centralized model and artifact catalog, and metadata-driven pipeline templates to standardize data science workflows. Fusionex investigated configuring Google Cloud AI Hub to surface reusable model assets and pipeline definitions for recurrent analytics workloads, supporting reproducible training and deployment patterns.
Integration work centered on linking Google Cloud AI tooling with Fusionexs proprietary analytics platform, creating a cloud-hosted catalog and pipeline registry accessible to regional data science teams. The implementation narrative indicates an operational scope that extends across ASEAN, with the intent that model and pipeline assets be consumable by customer-facing analytics environments and regional internal teams.
Governance activity described in the case study emphasized standardizing model packaging, lifecycle controls, and reuse policies to reduce duplication of effort across projects, while orchestration and versioning discipline were positioned as operational priorities. Outcomes noted in the source include faster processing and platform improvements for customers, and the deployment of Google Cloud AI Hub is explicitly characterized as an exploratory next phase rather than a completed large scale rollout.
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