List of BigML Customers
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Since 2010, our global team of researchers has been studying BigML 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 BigML 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 BigML for ML and Data Science Platforms include: Novo Nordisk, a Denmark based Life Sciences organisation with 78554 employees and revenues of $45.92 billion, Telefonica, a Spain based Communications organisation with 100870 employees and revenues of $42.96 billion, ABN AMRO, a Netherlands based Banking and Financial Services organisation with 22267 employees and revenues of $10.44 billion, T-Systems, a Germany based Professional Services organisation with 28000 employees and revenues of $4.70 billion, Avast, a Czech Republic based Professional Services organisation with 1750 employees and revenues of $1.00 billion and many others.
Contact us if you need a completed and verified list of companies using BigML, 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 Machine Learning software purchases.
The BigML 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 Machine Learning 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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ABN AMRO | Banking and Financial Services | 22267 | $10.4B | Netherlands | BigML | BigML | ML and Data Science Platforms | 2015 | n/a |
In 2015, ABN AMRO implemented BigML as a core ML and Data Science Platforms capability to advance its People Analytics agenda. The deployment introduced BigML to the bank's HR analytics function and positioned the vendor platform as the primary tool for machine learning driven analyses and data mining used by HR teams.
The implementation focused on platform capabilities typical of ML and Data Science Platforms, including supervised model training, feature engineering pipelines, model scoring workflows, and automated data mining processes aligned to HR use cases. BigML was configured to support iterative model development and to operationalize predictive analytics workflows that address talent, retention, and workforce insights.
Implementation partners included iNostix by Deloitte, who helped ABN AMRO build the necessary capabilities and perform analyses while BigML provided the ML platform. The platform was integrated into People Analytics workflows to consume HR data sources, generate model outputs, and feed those outputs into reporting and decision inputs consumed by senior leaders.
Governance and rollout were led from HR analytics under Auke IJsselstein, who presented the team’s fact based HR proposition and hard learned lessons at the 2ML conference. The work established structured model lifecycle practices and a repeatable approach for bringing machine learning and data mining insights into senior leadership decision making.
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Avast | Professional Services | 1750 | $1.0B | Czech Republic | BigML | BigML | ML and Data Science Platforms | 2016 | n/a |
In 2016 Avast implemented BigML as part of its analytics and data science tooling. BigML is used by Avast within the ML and Data Science Platforms category to lower barriers to machine learning and to enable analysts, software developers, and data scientists to build end to end predictive workflows.
The implementation focused on core platform capabilities typical of ML and Data Science Platforms, including dataset preparation, feature engineering, supervised model training, model evaluation, model export and model deployment workflows. Avast configured BigML to support automated model pipelines and repeatable experiments, leveraging the platform's programmatic interfaces and orchestration features to standardize model creation and validation processes.
Operational ownership rested with Avast data science and analytics teams and extended into product and security analytics functions, with a phased adoption approach across teams in 2016. Governance centered on model lifecycle management, controlled access and role based permissions, and process changes to embed machine learning workflows into existing analytics operations.
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Bankable Frontier Associates | Banking and Financial Services | 600 | $60M | United States | BigML | BigML | ML and Data Science Platforms | 2016 | n/a |
In 2016, Bankable Frontier Associates implemented BigML, adopting the BigML ML and Data Science Platforms to support analytics, predictive modeling, and risk scoring functions within its banking and financial services operations. The rollout focused on enabling analysts, software developers, and data scientists to operationalize machine learning workflows, positioning Bankable Frontier Associates BigML ML and Data Science Platforms Business Function as a centralized analytics capability rather than an ad hoc set of scripts.
The implementation leveraged standard ML and Data Science Platforms capabilities, including cloud-hosted model training, automated data preprocessing pipelines, supervised learning workflows, model evaluation and selection, and API-based prediction endpoints for batch and real time scoring. Operational coverage emphasized analytics and product teams, with governance centered on model management, versioning, and role based access controls to support reproducible experiments and controlled model promotion. Integrations were executed through API connections to existing data pipelines and ETL processes, and deployment architecture relied on hosted BigML services to reduce infrastructure overhead for the 600 person organization.
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Professional Services | 30 | $3M | United States | BigML | BigML | ML and Data Science Platforms | 2016 | n/a |
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Life Sciences | 78554 | $45.9B | Denmark | BigML | BigML | ML and Data Science Platforms | 2016 | n/a |
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Professional Services | 80 | $8M | United States | BigML | BigML | ML and Data Science Platforms | 2015 | n/a |
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Professional Services | 28000 | $4.7B | Germany | BigML | BigML | ML and Data Science Platforms | 2016 | n/a |
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Communications | 100870 | $43.0B | Spain | BigML | BigML | ML and Data Science Platforms | 2015 | n/a |
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Buyer Intent: Companies Evaluating BigML
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