DataRobot AI Accelerators are repeatable, code-first workflows designed to help speed up model development, deployment and time to value using the DataRobot API.
| Section | Details |
|---|---|
| Use Cases and Horizontal Approaches | Applied approaches to specific business challenges and general frameworks for broad classes of machine learning problems |
| Generative AI | All things generative + predictive AI |
| Ecosystem Integration Templates | Boilerplate templates to for end-to-end API workflows between DataRobot and our ecosystem partners like Snowflake, GCP, Azure, AWS, etc. |
| Advanced ML and API Approaches | Advanced usage of the DataRobot API you can inject in to your experiment workflow |
Follow our channel @DataRobot. For more tutorials and demonstrations, checkout the Generative AI + DataRobot and DataRobot AI Accelerators playlists. More videos coming soon!
Install the DataRobot Python Client Package.
- Clone this repo
- Import the desired accelerator into your preferred notebook (e.g., jupyter, Kaggle, Databricks Notebooks, Google Colab). We recommend using DR-Notebooks.
- Execute the notebook.
- Learn and understand the accelerator content.
- You should now be able to modify the accelerator to solve your own problem. The easiest place to start is to replace the input data with your own.
Please report feedback and problems by opening a Github Issue in this repo. Please note: The code in these repos is sourced from the DataRobot user community and is not owned or maintained by DataRobot, Inc. You may need to make edits or updates for this code to function properly in your environment.