DataRobot's Generative AI capabilities are a premium feature; contact your DataRobot representative for enablement information. Try this functionality for yourself in a limited capacity in the DataRobot trial experience.
Working with generative AI (GenAI) in DataRobot can include creating vector databases, creating and comparing LLM blueprints in the playground, preparing LLM blueprints for deployment, and optionally bringing your own LLM.
See the list of considerations to keep in mind when working with DataRobot GenAI.
|The steps for using DataRobot GenAI with or without your own data.
|GenAI in DataRobot overview
|An overview of the DataRobot GenAI workflow.
|Create vector databases
|Creating the vector database configuration.
|Create LLMs in the playground
|Creating LLMs and fine-tuning results.
|Compare LLM blueprints in the playground
|Deploy LLMs to production
|Registering LLMs for deployment.
|End-to-end code-first generative AI experimentation
|A comprehensive overview of the generative AI features DataRobot has to offer with the Python SDK.
|Create external vector databases with code
|How to build, validate, and register an external vector database to the DataRobot platform using DataRobot's Python SDK.
|Terms relevant to DataRobot GenAI capabilities.
|Criteria to keep in mind when working with DataRobot GenAI AI capabilities.
|Error handling for common playground and VDB situations.