Ecosystem integration templates¶
|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
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