Highlights : About DataRobot
Integration with enterprise security technologies
Distributed and self-healing architecture
Data accuracy
Hadoop cluster plug and play
Numerous database certifications
Ecosystem of algorithms
Product Details
Features
Automated machine learning
Speed
Ecosystem of algorithms
ETL and visualization tools
Numerous database certifications
Hadoop cluster plug and play
Data accuracy
Ease of use
Data preparation
Integration with enterprise security technologies
Distributed and self-healing architecture
Benefits
DataRobot is an ideal predictive analysis platform for any organization looking to become AI-driven.
It’s a powerful solution that offers enterprise-class automated machine learning techniques designed to help you transform your organization into an AI-driven enterprise without a hassle. It automates and streamlines your modeling lifecycle, enabling you to expediently and easily develop accurate predictive models with little or no coding and machine learning skills.
The platform is built to support rapid deployment of predictive models.
It allows you to implement models with a few clicks to derive true business value in minutes. All models designed using DataRobot publish a REST API endpoint, which means integrating them within your modern enterprise applications is a breeze. Also, with DataRobot, there is no struggle writing codes or spending months trying to leverage your existing infrastructure. The application makes it easy to create, deploy, and customize your models to get precise predictions at scale regardless of the size of your organization.
Additionally, DataRobot supports both on-premise and cloud-based deployment.
It offers a comprehensive cloud module powered by the Amazon Web Services (AWS) to add agility, flexibility, and convenience to machine learning. The integrated cloud environment ensures a low total cost of ownership by trashing the need for infrastructure setup, hardware installation, and additional computing cost.
The solution is also designed to leverage modern distributed data processing.
It runs multiple experiments simultaneously to radically minimize the time it takes to complete a data science project. Better still, it eliminates model deployment bottlenecks by offering different methods to deploy finished predictive models such as exportable prediction code, native and batch scoring, and prediction APIs for real-time scoring.