List of MLRun Customers
Since 2010, our global team of researchers has been studying MLRun 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 MLRun 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 MLRun for ML and Data Science Platforms include: Seagate Technology, a Singapore based Manufacturing organisation with 30000 employees and revenues of $6.55 billion, NetApp, a United States based Professional Services organisation with 11800 employees and revenues of $6.27 billion, Payoneer, a United States based Banking and Financial Services organisation with 2167 employees and revenues of $831.0 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 MLRun, 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 MLRun 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 | Insight Source |
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Falkonry | Professional Services | 70 | $11M | United States | MLRun | MLRun | ML and Data Science Platforms | 2021 | n/a | In 2021 Falkonry implemented MLRun within its ML and Data Science Platforms footprint, deploying MLRun alongside Kubeflow Pipelines on a Kubernetes cluster based in Sunnyvale CA. The project established an automated and scalable pipeline orchestration layer for time series model development and feature engineering. The implementation centered on pipeline orchestration and a custom time series featurizer, using a Scattering Transform to produce translation invariant representations for classification. MLRun was used to codify pipeline definitions and automate execution, while Kubeflow Pipelines provided workflow orchestration and step sequencing. Development work employed Python, Kymatio, and PyTorch to build and validate the featurizer and training pipelines. Integrations and operational scope include Kubernetes for container orchestration, Kubeflow Pipelines for workflow execution, and MLRun for pipeline runtime and function management. The deployment targeted Falkonry data science and machine learning engineering workflows, standardizing feature engineering and model training processes. The orchestrated pipelines reduced manual effort, with the team reporting savings of over 200 work hours per month since the 2021 deployment. | |
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NetApp | Professional Services | 11800 | $6.3B | United States | MLRun | MLRun | ML and Data Science Platforms | 2020 | n/a | In 2020, NetApp deployed the Iguazio data science platform, which incorporates MLRun, to power its Active IQ digital advisor for real-time predictive maintenance and storage analytics. The implementation positioned MLRun as the MLOps orchestration layer inside Iguazio to support production model serving for storage telemetry and anomaly detection workloads. MLRun was used within the broader ML and Data Science Platforms footprint to orchestrate model lifecycle activities including experiment orchestration, pipeline automation, model packaging, and real-time inference. Configuration centered on reusable pipelines and automated deployment workflows to move models from experimentation into the Active IQ serving layer, aligning platform capabilities with typical MLOps functions such as experiment tracking and pipeline scheduling. The deployment directly integrated the Iguazio runtime, including MLRun, with NetApp Active IQ to deliver storage analytics and predictive maintenance use cases, keeping the system focused on operational AI for storage products. Operational coverage emphasized AI service deployment into the Active IQ product, with the platform handling online inference and analytics ingestion for storage telemetry. Governance and process changes concentrated on operationalizing model release workflows and instituting pipeline-based deployments to production, enabling more rapid AI service delivery. Outcomes cited by NetApp include faster AI service deployment and major cost and storage efficiencies achieved through the combined Iguazio and MLRun implementation. | |
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Payoneer | Banking and Financial Services | 2167 | $831M | United States | MLRun | MLRun | ML and Data Science Platforms | 2020 | n/a | In 2020, Payoneer deployed the Iguazio MLOps platform to shift from reactive fraud detection to real-time predictive fraud prevention, instrumenting production machine learning that analyzes fresh transaction data to reduce fraud exposure. The Iguazio platform includes MLRun as its MLOps orchestration framework, and MLRun was used as the application orchestration layer within this ML and Data Science Platforms implementation. Implementation focused on production model orchestration, real-time inference pipelines, automated training workflows, and model serving for transaction-level scoring. MLRun provided pipeline orchestration and experiment tracking functionality consistent with ML and Data Science Platforms, enabling automated retraining and rollout of production models that operate on streaming transaction inputs. Operational scope centered on fraud prevention and risk management workflows, with deployed models integrated into transaction processing paths to provide near real-time scoring for Payoneer payment flows. The work impacted fraud operations and risk teams by embedding model-driven decisioning into operational workflows and production monitoring. Governance emphasized operationalizing MLOps practices, including continuous pipeline automation, model versioning and monitoring, and production change controls typical of MLRun-driven deployments. The announced deployment delivered reduced fraud exposure as an explicit outcome of the productionized real-time ML capabilities. | |
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Manufacturing | 30000 | $6.6B | Singapore | MLRun | MLRun | ML and Data Science Platforms | 2022 | n/a |
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Buyer Intent: Companies Evaluating MLRun
- Dz Bank Ag Deutsche Zentral Genossenschaftsbank Frankfurt Am Main, a Hong Kong based Banking and Financial Services organization with 50 Employees
- World Relief, a United States based Non Profit company with 800 Employees
Discover Software Buyers actively Evaluating Enterprise Applications
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