List of Kubeflow Pipelines Customers
Since 2010, our global team of researchers has been studying Kubeflow Pipelines 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 Kubeflow Pipelines for Apps Development 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 Kubeflow Pipelines for Apps Development include: Runway, a United States based Professional Services organisation with 450 employees and revenues of $50.0 million, Falkonry, a United States based Professional Services organisation with 70 employees and revenues of $11.0 million and many others.
Contact us if you need a completed and verified list of companies using Kubeflow Pipelines, 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 Kubeflow Pipelines 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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Falkonry | Professional Services | 70 | $11M | United States | Kubeflow | Kubeflow Pipelines | Apps Development | 2021 | n/a |
In 2021, Falkonry Inc in Sunnyvale, CA deployed Kubeflow Pipelines as part of an Apps Development initiative to orchestrate machine learning workflows on Kubernetes. The deployment integrated Kubeflow Pipelines with MLRun and the Kubernetes platform to enable automated, scalable pipeline orchestration for time series modeling. The effort targeted data science and ML engineering functions responsible for model development and operationalization.
Implementation included a featurizer for time series data using Scattering Transform to learn translation invariant representations, built with Python, Kymatio and Pytorch, orchestrated through Kubeflow Pipelines and MLRun on Kubernetes. Functional capabilities implemented included automated experiment tracking, pipeline scheduling and containerized task execution consistent with Apps Development pipeline orchestration patterns. Governance changes centralized pipeline definitions and experiment artifacts under MLRun, reducing ad hoc script workflows. The Kubeflow Pipelines deployment delivered automated pipeline orchestration at scale and explicitly reported savings of over 200 work hours monthly.
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Runway | Professional Services | 450 | $50M | United States | Kubeflow | Kubeflow Pipelines | Apps Development | 2020 | n/a |
In 2020, Runway implemented Kubeflow Pipelines to orchestrate machine learning workflows for its generative content creation platform. The deployment aligned with the Apps Development category and focused on operationalizing model training, validation, and inference pipelines to address the rising compute demands from Runway's web and desktop creator tools.
The Kubeflow Pipelines implementation emphasized canonical pipeline components, including data preprocessing, model training stages, model optimization steps, and automated model packaging for serving. Runway incorporated explicit quantization and architecture optimization steps into pipeline stages, reflecting the applied changes of INT8 quantization and model architecture modification used to accelerate a person segmentation model.
Runway integrated Kubeflow Pipelines with Intel's Deep Learning Reference Stack as part of a co-development effort with Intel, enabling binary and runtime optimizations on the inference path. Operational ownership covered research and platform engineering teams, and the CI/CD oriented pipeline design supported repeatable model builds, validation gates, and controlled promotion into cloud inference clusters.
Governance centered on pipeline-driven validation and rollout controls, with model versioning and artifact promotion embedded in the workflow to limit risky deployments. As an explicit outcome of the combined Kubeflow Pipelines orchestration and Intel stack optimizations, Runway achieved more than 4x performance improvement on the targeted person segmentation model, materially reducing inference serving costs on its cloud infrastructure.
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