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About DataRobot

DataRobot is an AI platform from DataRobot that delivers industry-leading applications designed to maximize impact and minimize risk for businesses. It provides features such as comprehensive AI insights, reliable support for AI practitioners, and recognition through validation awards, so users can better navigate their AI journeys. The platform is deliberately designed to unify complex enterprise environments, ensuring that every team can use its capabilities effectively. DataRobot also helps power each phase of the agent lifecycle, from initial deployment to ongoing management. Key capabilities: AI insights Support for AI practitioners Validation awards Enterprise environment integration Lifecycle management Best for: businesses that need a comprehensive platform to implement and manage AI solutions.

DataRobot Details

Vendor
DataRobot
Year Launched
2012
Location
Boston, Massachusetts
Deployment
cloud
Training Options
documentation, videos, demo
Countries Served
All Countries
Languages
English
Users
Data scientists, ML engineers, AI engineers, Business analysts, IT and security teams, DevOps teams
Industries Served
Energy, Financial Services, Government, Healthcare, Manufacturing, Telecommunications
Tags
Data Science Platforms, Machine Learning Platforms, MLOps, AI Platform

DataRobot 's In-App Market Place

Does DataRobot have an in-app market place?

No

How many Mini-Apps in the marketplace?

1

Mini Apps

N/A

Pricing Options

Free trial
Free version
Request a quote
Promo Offer

Accepted Payment Currencies

USD($), EUR (€), GBP (£)

Pros & Cons

  • Automates machine learning model building deployment and monitoring workflows
  • Supports multiple data types structured unstructured and time series
  • Rapid model training accelerates data science project timelines significantly
  • Built in explainability tools improve trust and regulatory compliance needs
  • Scales well for enterprise level data volumes and use cases
  • Black box perception remains despite explainability tools provided
  • Custom model tuning may feel constrained for advanced data scientists
  • Requires clean high quality data to deliver strong results
  • Platform complexity may overwhelm teams with simple modeling needs
  • Automated approach may hide underlying modeling assumptions from users

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