List of BigML Customers
BigML
2851 NW 9th St, Suite D,
Corvallis, 97330, OR,
United States
Corvallis, 97330, OR,
United States
1 866-916-4218
Professional Services
26
$3M
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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Faraday | Professional Services | 30 | $3M | United States | BigML | BigML | ML and Data Science Platforms | 2016 | n/a |
In 2016, Faraday implemented BigML to establish a formal machine learning capability within the company. Faraday is a United States based professional services firm with 30 employees, and the deployment positioned BigML as the ML and Data Science Platforms component for its analytics and client delivery functions.
The implementation focused on core ML and Data Science Platforms capabilities including data ingestion and preparation, model training and evaluation, automated model selection workflows, and API-based scoring for production use. BigML was configured to support iterative supervised modeling, model export and persistent model artifacts, and scheduled workflows to operationalize recurring training and scoring tasks.
Operationally the deployment followed a centralized analytics architecture with BigML providing SaaS style model orchestration and API endpoints for prediction services, enabling integration points into Faraday professional services workflows and client engagements. Governance centered on model versioning, experiment tracking, and role based access for analysts and developers, with rollout scope covering analytics, client delivery, and product-facing prediction pipelines.
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Novo Nordisk | Life Sciences | 78554 | $45.9B | Denmark | BigML | BigML | ML and Data Science Platforms | 2016 | n/a |
In 2016, Novo Nordisk implemented BigML to add cloud-accessible machine learning capabilities across its analytics and research workflows. The deployment introduced BigML as an ML and Data Science Platforms application to support analysts, software developers, and scientists working on model development and predictive analytics.
The BigML implementation centered on platform-native model lifecycle capabilities, including model training, supervised and unsupervised algorithms, automated workflows for model building and evaluation, ensemble methods and model selection, and production-grade prediction endpoints. Configuration emphasized reusable experiment artifacts and automated pipelines that align with common ML and Data Science Platforms functional patterns, enabling repeatable model training, validation, and deployment processes.
Operational coverage targeted data science, analytics and software engineering teams, with integration points exposed via BigML APIs to embed prediction services into existing development and analytics pipelines. Governance focused on role-based access controls, model versioning and lifecycle stewardship to support iterative model refinement across teams, while adoption followed a developer and analyst centric growth pattern consistent with BigML’s grassroots user approach.
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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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| Logo | Company | Industry | Employees | Revenue | Country | Evaluated | ||
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FAQ - APPS RUN THE WORLD BigML Coverage
BigML is a ML and Data Science Platforms solution from BigML.
Companies worldwide use BigML, from small firms to large enterprises across 21+ industries.
Organizations such as Novo Nordisk, Telefonica, ABN AMRO, T-Systems and Avast are recorded users of
BigML for ML and Data Science Platforms.
Companies using BigML are most concentrated in Life Sciences, Communications and Banking and Financial Services, with adoption spanning over 21 industries.
Companies using BigML are most concentrated in Denmark, Spain and Netherlands, with adoption tracked across 195 countries worldwide. This global distribution highlights the popularity of BigML across Americas, EMEA, and APAC.
Companies using BigML range from small businesses with 0-100 employees - 25%, to mid-sized firms with 101-1,000 employees - 12.5%, large organizations with 1,001-10,000 employees - 12.5%, and global enterprises with 10,000+ employees - 50%.
Customers of BigML include firms across all revenue levels — from $0-100M, to $101M-$1B, $1B-$10B, and $10B+ global corporations.
Contact APPS RUN THE WORLD to access the full verified BigML customer database with detailed Firmographics such as industry, geography, revenue, and employee breakdowns as well as key decision makers in charge of ML and Data Science Platforms.