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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CareerBuilder | Professional Services | 3300 | $965M | United States | Google Cloud Machine Learning Engine | ML and Data Science Platforms | 2016 | n/a | In 2016, CareerBuilder implemented Google Cloud Machine Learning Engine to enhance job search relevance on careerbuilder.com and across clients' talent networks. The deployment used Google Cloud Machine Learning Engine within the ML and Data Science Platforms category to operationalize models for relevance, matching, and ranking. The implementation centralized model training and serving pipelines, employing the Google Cloud Machine Learning Engine for scalable model training, hyperparameter tuning, and prediction serving. Functional capabilities included feature extraction from job postings and candidate profiles, semantic matching models for job-candidate relevance, and both batch and online inference to support search and recommendation workflows. Configuration aligned with ML and Data Science Platforms practices, including model versioning and automated deployment workflows. CareerBuilder integrated Google Cloud Talent Solution alongside Google Cloud Machine Learning Engine to combine talent-specific indexing with machine-learned relevance signals. Operational scope covered careerbuilder.com and clients' talent networks, and business functions impacted included consumer job search on the public site and talent acquisition tools delivered to client employers. Data pipelines ingested job listings and candidate interaction signals into Google Cloud for training and runtime scoring. The vendor-provided Google machine learning capabilities were used to continuously refine search relevance, and CareerBuilder reported improved job search results on careerbuilder.com and across clients’ talent networks. This implementation exemplifies use of Google Cloud Machine Learning Engine within ML and Data Science Platforms to support production relevance and matching use cases for a commercial job board. | ||
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Dialpad | Professional Services | 1000 | $200M | United States | Google Cloud Machine Learning Engine | ML and Data Science Platforms | 2016 | n/a | In 2016, Dialpad implemented Google Cloud Machine Learning Engine on Google Cloud Platform to accelerate product development and scale data science workflows. Google Cloud Machine Learning Engine was deployed as the core infrastructure within the ML and Data Science Platforms category to provide managed model training and serving capabilities for Dialpad's product features. The implementation focused on managed training jobs, model versioning, and prediction serving pipelines, leveraging the Google Cloud Machine Learning Engine's scalable compute and orchestration capabilities. Data ingestion and preprocessing pipelines were centralized on Google Cloud Platform to feed ML workflows, enabling engineering and data science teams to iterate on models and push updates into production more rapidly. Operational coverage included core product engineering and data science functions across Dialpad in the United States, aligning model lifecycle activities with product release cycles. Governance emphasized reproducible training pipelines and model lifecycle controls to limit operational overhead, allowing the company to concentrate on customer-facing improvements. Dialpad reported that developing on Google Cloud Platform allowed faster growth and improved productivity, and that without Google Cloud Platform they would have needed twice as many people to achieve the same velocity. | ||
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Professional Services | 13 | $2M | Sweden | Google Cloud Machine Learning Engine | ML and Data Science Platforms | 2016 | n/a |
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Professional Services | 40 | $10M | United States | Google Cloud Machine Learning Engine | ML and Data Science Platforms | 2016 | n/a |
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Professional Services | 100 | $20M | Austria | Google Cloud Machine Learning Engine | ML and Data Science Platforms | 2016 | n/a |
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Professional Services | 20 | $2M | United States | Google Cloud Machine Learning Engine | ML and Data Science Platforms | 2016 | n/a |
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Professional Services | 60 | $10M | Germany | Google Cloud Machine Learning Engine | ML and Data Science Platforms | 2016 | n/a |
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Professional Services | 110 | $22M | Singapore | Google Cloud Machine Learning Engine | ML and Data Science Platforms | 2016 | n/a |
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Professional Services | 700 | $120M | United Kingdom | Google Cloud Machine Learning Engine | ML and Data Science Platforms | 2016 | n/a |
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Professional Services | 300 | $40M | Norway | Google Cloud Machine Learning Engine | ML and Data Science Platforms | 2016 | n/a |
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