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DataRobot API resources > AI accelerators > Ecosystem integration templates

Ecosystem integration templates

Topic Describes...
End-to-end ML workflow with Databricks Build models in DataRobot with data acquired and prepared in a Spark-backed notebook environment provided by Databricks.
End-to-end ML workflow with Google Cloud Platform and BigQuery Use Google Collaboratory to source data from BigQuery, build and evaluate a model using DataRobot, and deploy predictions from that model back into BigQuery and GCP.
End-to-end ML workflow with Snowflake Work with Snowflake and DataRobot's Python client to import data, build and evaluate models, and deploy a model into production to make new predictions.
End-to-end ML workflow with AWS Work with AWS and DataRobot's Python client to import data, build and evaluate models, and deploy a model into production to make new predictions.
End-to-end ML workflow with Azure Work with Azure and DataRobot's Python client to import data, build and evaluate models, and deploy a model into production to make new predictions.
Monitor AWS Sagemaker models with MLOps Train and host a SageMaker model that can be monitored in the DataRobot platform.
Integrate DataRobot and Snowpark by maximizing the data cloud Leverage Snowflake for data storage and Snowpark for deployment, feature engineering, and model scoring with DataRobot.
End-to-end workflow with SAP Hana Learn how to programmatically build a model with DataRobot using SAP Hana as the data source.
Deploy Scoring Code as a microservice Follow a step-by-step procedure to embed Scoring Code in a microservice and prepare it as the Docker container for a deployment on customer infrastructure (it can be self- or hyperscaler-managed K8s).
End-to-end demand forecasting workflow with DataRobot and Databricks How to use DataRobot with Databricks to develop, evaluate, and deploy a multi-series demand forecasting model.
Create and deploy a custom model 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.
Integrate GraphQL with DataRobot Connect a GraphQL server to the DataRobot OpenAPI specification using GraphQL Mesh.
Updated January 31, 2024