AI Accelerators Library

This accelerator outlines how to create, deploy, and monitor a custom inference model with DataRobot's Python client. You can use the Custom Model Workshop to upload a model artifact to create, test, and deploy custom inference models to DataRobot’s centralized deployment hub.

This accelerator is developed for use with Databricks to help users leverage the power of DataRobot for time-series modeling within their Databricks ecosystem.

Level up your end-to-end ML lifecycle on the Data Cloud  by integrating DataRobot and Snowflake.

Source data from S3 or Athena, build and evaluate ML models using DataRobot, send predictions back to S3.

How data stored in Azure can be used to train a collection of models.

Import data, build and evaluate models, and deploy a model into production to make new predictions with Snowflake.

Build and refine ML models within DataRobot and deploy them to run within AWS SageMaker.

Integrate directly into your GCP environment to accelerate your use of machine learning across all of the GCP services.

Pair the power of DataRobot with the Spark-backed notebook environment provided by Databricks.

Sample solution for monitoring AWS Sagemaker models with DataRobot MLOps.

Use DataRobot and the Python API to build a workflow with SAP as the remote data source.

Embed scoring code in a microservice and prepare as Docker container.