| 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. |
| End-to-end ML workflow with Athena |
Read in an Amazon Athena table to create a project and deploy a model to make predictions with a test dataset. |
| End-to-end ML workflow with Sagemaker |
Take an ML model that has been built with DataRobot and deploy it to run within AWS SageMaker. |