List of Polyaxon Cloud Customers
Since 2010, our global team of researchers has been studying Polyaxon Cloud 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 Polyaxon Cloud 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 Polyaxon Cloud for ML and Data Science Platforms include: Mercari, a Japan based Professional Services organisation with 2209 employees and revenues of $1.10 billion, Netguru, a United States based Professional Services organisation with 450 employees and revenues of $80.0 million, Intuition Machines, a United States based Professional Services organisation with 150 employees and revenues of $40.0 million and many others.
Contact us if you need a completed and verified list of companies using Polyaxon Cloud, 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.
The Polyaxon Cloud customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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Intuition Machines | Professional Services | 150 | $40M | United States | Polyaxon | Polyaxon Cloud | ML and Data Science Platforms | 2019 | n/a |
In 2019, Intuition Machines integrated Polyaxon Cloud into its Azure Kubernetes-based ML infrastructure. The Polyaxon Cloud implementation served as the company's ML and Data Science Platforms layer, centralizing experiment lifecycle management for research and production workloads.
The deployment implemented core capabilities including experiment tracking, single-sign-on access, and the conversion of interactive Jupyter notebooks into orchestrated distributed training jobs. Experiment tracking captured runs, parameters and artifacts to improve reproducibility, while notebook-to-job conversion automated packaging, container execution and job scheduling for large-scale training.
Architecturally, Polyaxon Cloud was deployed on Intuition Machines' Azure Kubernetes Service cluster, leveraging container orchestration and cluster resource scheduling to run multi-node training workflows. The implementation enabled multi-user experiment management through access controls and workspace separation, and integrated single-sign-on to provide centralized authentication across data science teams.
The US-based implementation focused on ML training and infrastructure and impacted data science, ML engineering and research functions. Governance emphasized standardized experiment workflows and reproducibility practices to support collaborative development and large-scale distributed training workflows.
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Mercari | Professional Services | 2209 | $1.1B | Japan | Polyaxon | Polyaxon Cloud | ML and Data Science Platforms | 2023 | n/a |
In 2023, Mercari implemented Polyaxon Cloud as part of its ML and Data Science Platforms to run machine learning pipelines and manage experiment orchestration. Polyaxon Cloud is used by Mercari US to centralize experiment orchestration and scale training workloads across its GKE-based ML cluster.
The deployment architecture pairs Polyaxon Cloud with Kubeflow Pipelines on Google Kubernetes Engine, where Polyaxon handles experiment orchestration, autoscaling policies, and pipeline lifecycle management. Functional capabilities implemented include experiment orchestration and autoscaling, pipeline scheduling improvements, and orchestration of model training workflows consistent with ML and Data Science Platforms capabilities. The engineering team applied cluster-autoscaler and refined scheduling to optimize node utilization within the GKE cluster.
Integrations explicitly include Kubeflow Pipelines and Google Kubernetes Engine, with operational scope concentrated on Mercari US machine learning teams and training infrastructure. Governance moved toward orchestration-driven workflow control and scheduling adjustments to enforce autoscaling and resource allocation for experiments. The team reported significant infrastructure cost reductions after applying cluster-autoscaler and scheduling improvements.
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Netguru | Professional Services | 450 | $80M | United States | Polyaxon | Polyaxon Cloud | ML and Data Science Platforms | 2019 | n/a |
In 2019, Netguru implemented Polyaxon Cloud. The deployment positioned Polyaxon Cloud within Netguru's ML and Data Science Platforms stack to support ML development and infrastructure for Netguru's Poland engineering teams, aligning the application with the companys experiment orchestration and dataset management needs.
Polyaxon Cloud was used to provide experiment scheduling, experiment orchestration, dataset versioning workflows, and infrastructure maintenance capabilities. The implementation emphasized reproducible ML experiment pipelines and automated scheduling of model runs, using Polyaxon Cloud to centralize experiment configuration, logging, and lifecycle management.
The implementation integrated Polyaxon Cloud with Quilt for dataset versioning as described in a PyData Warsaw 2019 presentation, enabling tracked datasets alongside orchestrated experiments. The architecture relied on Polyaxon Cloud as the orchestration layer for compute and data artifacts, coordinating model training jobs and dataset snapshots across development projects.
Netguru formalized an ML workflow practice that covered both ML development and infrastructure, and reportedly improved project reproducibility and accelerated team delivery through Polyaxon-driven experiment orchestration. Governance focused on standardized experiment definitions and dataset versioning policies to sustain reproducibility within the ML and Data Science Platforms environment.
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