DataRobot
Overview
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HQ Location
United States
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Year Founded
2012
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Company Type
Private
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Revenue
$10-100m
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Employees
201 - 1,000
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Website
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Twitter Handle
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Company Description
DataRobot offers a Machine Learning platform for data scientists of all skill levels to build and deploy accurate predictive models in a fraction of the time it used to take. The technology addresses the critical shortage of data scientists by changing the speed and economics of predictive analytics.The DataRobot platform uses massively parallel processing to train and evaluate 1000's of models in R, Python, Spark MLlib, H2O and other open-source libraries. It searches through millions of possible combinations of algorithms, pre-processing steps, features, transformations, and tuning parameters to deliver the best models for your dataset and prediction target. The DataRobot platform evaluates hundreds of cutting-edge Machine Learning algorithms to discover, deploy, and customize the best Machine Learning models for every situation. It also delivers the most accurate insights at scale, providing the fastest path to Data Science success for organizations of all sizes. DataRobot was founded in June 2012 and is headquartered in Boston, Massachusetts, USA.
IoT Solutions
Automated Machine Learning
Automate the creation of advanced Machine Learning models that incorporate our world-class Data Science expertise.
Automated Time Series
Automate the development of sophisticated time series models that predict the future values of a data series based on its history and trend. Organizations of all sizes will improve forecasts for sales volume, product demand by SKU, staffing, inventory, and a host of financial applications.
MLOps
Delivering the capabilities that Data Science and IT Ops teams need to work together to deploy, monitor, and manage Machine Learning models in production.
Automate the creation of advanced Machine Learning models that incorporate our world-class Data Science expertise.
Automated Time Series
Automate the development of sophisticated time series models that predict the future values of a data series based on its history and trend. Organizations of all sizes will improve forecasts for sales volume, product demand by SKU, staffing, inventory, and a host of financial applications.
MLOps
Delivering the capabilities that Data Science and IT Ops teams need to work together to deploy, monitor, and manage Machine Learning models in production.
Key Customers
Accenture, Aegon, Deloitte, Havard Business School, Panasonic, Lenovo, United Airlines
IoT Snapshot
DataRobot is a provider of Industrial IoT infrastructure as a service (iaas), platform as a service (paas), and analytics and modeling technologies, and also active in the finance and insurance, healthcare and hospitals, oil and gas, retail, and security and public safety industries.
Technologies
Use Cases
Functional Areas
Industries
Services
Technology Stack
DataRobot’s Technology Stack maps DataRobot’s participation in the infrastructure as a service (iaas), platform as a service (paas), and analytics and modeling IoT Technology stack.
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Devices Layer
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Edge Layer
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Cloud Layer
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Application Layer
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Supporting Technologies
Technological Capability:
None
Minor
Moderate
Strong
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Case Studies.
Case Study
Democratizing Data Science at DemystData
DemystData wanted a way to handle the increased complexity and time-intensive work related to datasets getting bigger and data sources becoming more varied. Their data scientists were spending lots of time manually building data science and machine learning pipelines.DemystData aims to close that gap and help solve the problem by opening up their clients’ access to new and more data. But as datasets get bigger and data sources more varied, that also means increased complexity and more time-consuming work for an already limited pool of data science resources at the New York-based company.
Case Study
Accelerates Data Discovery, Testing, and Deployment
As datasets get bigger and data sources more varied, complexity increases and work processes become more time-consuming. Demyst clients need help identifying which external data attributes are predictive in marketing, risk, and portfolio management use cases across the vast ocean of external data.
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