List of Seldon Enterprise Platform Customers
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Since 2010, our global team of researchers has been studying Seldon Enterprise Platform 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 Seldon Enterprise Platform for MLOps 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 Seldon Enterprise Platform for MLOps Platforms include: Audi, a Germany based Automotive organisation with 88604 employees and revenues of $70.61 billion, PayPal, a United States based Banking and Financial Services organisation with 24400 employees and revenues of $31.80 billion, Experian UK, a United Kingdom based Professional Services organisation with 3600 employees and revenues of $813.0 million and many others.
Contact us if you need a completed and verified list of companies using Seldon Enterprise Platform, 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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Audi | Automotive | 88604 | $70.6B | Germany | Seldon | Seldon Enterprise Platform | MLOps Platforms | 2021 | n/a |
In 2021, Audi deployed Seldon Enterprise Platform to productionize machine learning models used by automotive engineering and operations in Germany. The Seldon Enterprise Platform was adopted as an MLOps Platforms solution to centralize model serving and monitoring capabilities and to operationalize inference workloads across engineering and operations teams.
The implementation emphasized model serving and monitoring modules, inferred from Seldon's product focus and public customer listings. Audi configured containerized model servers with scalable inference endpoints orchestrated on Kubernetes to manage runtime load and support rolling model updates, and instrumented runtime telemetry for latency and error observability across production endpoints.
Operational scope focused on Audi’s engineering and operations organizations in Germany, supporting vehicle engineering validation workflows and operational analytics. The Seldon Enterprise Platform was used to manage production inference pipelines, standardize deployment artifacts, and formalize handoffs between data science and production teams, covering both real-time and batch inference paths.
Governance centered on model monitoring, version control, and alerting workflows to establish lifecycle controls and incident response for production models. Implementing Seldon Enterprise Platform improved the reliability and scalability of Audi’s inference pipelines while providing consistent model serving and observability across automotive engineering and operations.
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Experian UK | Professional Services | 3600 | $813M | United Kingdom | Seldon | Seldon Enterprise Platform | MLOps Platforms | 2022 | n/a |
In 2022, Experian UK deployed Seldon Enterprise Platform to manage and monitor credit risk and analytics models in the United Kingdom. The implementation positioned Seldon Enterprise Platform within the MLOps Platforms category to support model lifecycle management for the companys analytics organization. The initial scope emphasized production model management for credit risk and related analytics use cases across Experian UK.
Configuration and module selection focused on model serving, monitoring and explainability, with the Seldon Enterprise Platform providing scalable inference endpoints, telemetry for performance and drift detection, and explainability outputs to support auditability. Operational coverage centered on credit risk and analytics teams, embedding the platform into model validation and release workflows and standardizing runtime observability. Governance workstreams accompanied the deployment to centralize monitoring, standardize explainability artifacts for model review, and formalize operational controls for production models.
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PayPal | Banking and Financial Services | 24400 | $31.8B | United States | Seldon | Seldon Enterprise Platform | MLOps Platforms | 2022 | n/a |
In 2022, PayPal deployed the Seldon Enterprise Platform to operationalize machine learning supporting payments and fraud-detection workflows in the United States. The Seldon Enterprise Platform, categorized as MLOps Platforms, was positioned as the production ML layer to standardize model lifecycle activities across engineering and risk functions.
The implementation emphasized model serving, monitoring and explainability capabilities to host and manage production models. PayPal configured model serving endpoints for real-time scoring, established observability pipelines for performance and data drift detection, and enabled explainability tooling to surface model behavior for fraud analysts and reviewers.
Operational coverage focused on payments and fraud-detection business functions within PayPal’s US operations, embedding model endpoints into transaction processing and fraud review workflows. The rollout impacted engineering teams responsible for production APIs, risk operations and fraud investigation teams that consume model outputs and monitoring dashboards.
Governance and process changes included standardized deployment workflows, a centralized model registry and runbook-driven monitoring and alerting to improve oversight and auditability. According to vendor disclosures, the Seldon Enterprise Platform deployment accelerated model deployment and improved governance for PayPal’s ML-supported payments and fraud workflows during the 2022 implementation.
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