DataRobot
Overview
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
Demystifying Data Science: A Case Study on DemystData and DataRobot
DemystData, a New York-based software company, is dedicated to demystifying data for its clients, particularly financial institutions. Despite the increasing use of data in the financial sector, it is still heavily underutilized, leading to business decisions being made based on suboptimal or incomplete data. DemystData aims to close this gap by providing clients with access to new and more data. However, as datasets grow larger and data sources become more varied, the complexity increases, leading to more time-consuming work for the limited pool of data science resources at the company. The challenge was to manage this increasing complexity and workload without compromising the quality of data analysis and insights.
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.
Case Study
84.51° Enhances Personalized Shopping Experience for Kroger Shoppers with DataRobot
84.51°, a retail data science, insights, and media company, was faced with the challenge of creating more personalized and valuable experiences for shoppers across the path to purchase. This included the entire customer journey, from initial awareness to activation, retention, and beyond. The company's goal was to leverage 1st-party retail data from over 62 million U.S. households to fuel a more customer-centric journey. However, the sheer volume of data and the complexity of creating personalized experiences for such a large customer base presented a significant challenge. The company needed a solution that would enable better production, deployment, and interpretation of data science models to meet its objectives.
Case Study
Flexiti Enhances Customer Insights with AI: A Case Study
Flexiti, a rapidly growing company in Canada, is recognized as the country's leading provider of point-of-sale financing with buy-now, pay-later solutions. Despite its success, the company faced a significant challenge. It sought to empower its talented risk and analytics team to gain greater visibility into data more quickly. The need for faster and more efficient data insights was crucial to maintain its competitive edge and continue its growth trajectory. The challenge was not only to speed up the data analysis process but also to ensure the accuracy and reliability of the insights derived from the data.
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