Apps Purchases: 10+ Million Software Purchases
Founded in 2010, APPS RUN THE WORLD is a leading technology intelligence and market-research company devoted to the application space. Leveraging a rigorous data-centric research methodology, we ask the simple B2B sales intelligence question: Who’s buying enterprise applications from whom and why?
Our global team of 50 researchers has been studying the digital transformation initiatives being undertaken by 2 million + companies including technographic segmentation of 10 million ERP, EPM, CRM, HCM, Procurement, SCM, Treasury software purchases, 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.
Apps Run The World Buyer Insight and Technographics Customer Database has over 100 data fields that detail company usage of emerging technologies such as AI, Machine Learning, IoT, Blockchain, Autonomous Database, and different on-prem and cloud apps by function, customer size (employees, revenues), industry, country, implementation status, year deal won, partner involvement, Line of Business Key Stakeholders and key decision-makers contact details, including the systems being used by Fortune 1000 and Global 2000 companies.
Apply Filters For 10+ Million Software Purchases
- Professional Services
| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | VAR/SI | Insight | Insight Source |
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Aggreko USA | Professional Services | 1000 | $15M | United States | Microsoft | Microsoft Azure Machine Learning | ML and Data Science Platforms | 2016 | n/a | In 2016, Aggreko USA implemented Microsoft Azure Machine Learning to power predictive maintenance across its rental power and temperature control fleets. The deployment used Microsoft Azure Machine Learning as the central ML and Data Science Platforms capability for model experimentation and production scoring. The implementation focused on model development workflows including experiment tracking, automated training pipelines, feature engineering and model deployment to production scoring endpoints. Teams used Microsoft Azure Machine Learning for supervised learning model lifecycle management, enabling repeatable training runs, model versioning and controlled promotion of models to production. Data pipelines ingested equipment telemetry streams and historical service records to feed training and scoring workflows, consuming Aggreko operational data sources. Operational scope covered service engineering, operations and field maintenance teams in the United States, aligning machine learning outputs to maintenance planning and dispatch workflows. Governance established model version control, deployment approvals and operational runbooks to support ongoing retraining and model stewardship. | |
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Jumptuit | Professional services | 10 | $1M | United States | Microsoft | Microsoft Azure Machine Learning | ML and Data Science Platforms | 2016 | n/a | ||
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Professional services | 100 | $10M | Denmark | Microsoft | Microsoft Azure Machine Learning | ML and Data Science Platforms | 2016 | n/a |
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Professional Services | 1700 | $325M | United States | Microsoft | Microsoft Azure Machine Learning | ML and Data Science Platforms | 2015 | n/a |
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Professional Services | 221000 | $243.0B | United States | Microsoft | Microsoft Azure Machine Learning | ML and Data Science Platforms | 2015 | n/a |
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Professional Services | 190 | $22M | United States | Microsoft | Microsoft Azure Machine Learning | ML and Data Science Platforms | 2015 | n/a |
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Professional services | 7 | $1M | United States | Microsoft | Microsoft Azure Machine Learning | ML and Data Science Platforms | 2016 | n/a |
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Professional services | 40 | $4M | United States | Microsoft | Microsoft Azure Machine Learning | ML and Data Science Platforms | 2016 | n/a |
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Professional services | 20 | $4M | United States | Microsoft | Microsoft Azure Machine Learning | ML and Data Science Platforms | 2016 | n/a |
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Professional Services | 7000 | $3.7B | South Korea | Microsoft | Microsoft Azure Machine Learning | ML and Data Science Platforms | 2017 | n/a |
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